From 18dc89f653f6bfbb0133c5cafdacf756e2136f7a Mon Sep 17 00:00:00 2001 From: huzhanbin <1042651817@qq.com> Date: Wed, 20 Mar 2024 17:07:33 +0800 Subject: [PATCH] v0.50 --- 404.html | 140 +++++++++++++-- algolia.json | 2 +- android-chrome-192x192.png | Bin 0 -> 37224 bytes android-chrome-384x384.png | Bin 0 -> 103149 bytes android-chrome-512x512.png | Bin 0 -> 261209 bytes apple-touch-icon.png | Bin 0 -> 33914 bytes at.png | Bin 0 -> 450740 bytes browserconfig.xml | 9 + categories/agent/index.html | 140 +++++++++++++-- categories/emergent-abilities/index.html | 140 +++++++++++++-- categories/index.html | 140 +++++++++++++-- categories/study-blog/index.html | 140 +++++++++++++-- categories/tast_cate/index.html | 140 +++++++++++++-- .../index.html" | 140 +++++++++++++-- ...4f241c74cecad2aabd9849a750e_1523816881.png | Bin 0 -> 285086 bytes favicon-16x16.png | Bin 0 -> 1377 bytes favicon-32x32.png | Bin 0 -> 2735 bytes favicon.ico | Bin 0 -> 15086 bytes index.html | 140 +++++++++++++-- mstile-150x150.png | Bin 0 -> 21390 bytes page/2/index.html | 140 +++++++++++++-- page/3/index.html | 140 +++++++++++++-- .../index.html | 156 ++++++++++++++--- posts/chatgpt/index.html | 154 +++++++++++++--- posts/emergent_abilities_of_llm/index.html | 142 +++++++++++++-- posts/fcn+ampamp+u-net/index.html | 152 +++++++++++++--- posts/fpn+ampamp+ssd+ampamp+yolo/index.html | 152 +++++++++++++--- posts/hpe/index.html | 140 +++++++++++++-- posts/index.html | 140 +++++++++++++-- posts/llm+p/index.html | 142 +++++++++++++-- .../index.html | 148 +++++++++++++--- posts/react/index.html | 154 +++++++++++++--- posts/resnet+ampamp+densenet/index.html | 162 +++++++++++++---- posts/retinanet+ampamp+fcos/index.html | 152 +++++++++++++--- posts/self-supervised+pretrain/index.html | 140 +++++++++++++-- posts/senet+ampamp+mobilenet/index.html | 164 ++++++++++++++---- .../shufflenet+ampamp+efficientnet/index.html | 152 +++++++++++++--- .../index.html" | 140 +++++++++++++-- safari-pinned-tab.svg | 62 +++++++ service-worker.min.js | 1 + site.webmanifest | 19 ++ tags/hpe/index.html | 140 +++++++++++++-- tags/index.html | 140 +++++++++++++-- tags/llm/index.html | 140 +++++++++++++-- tags/nlp/index.html | 140 +++++++++++++-- tags/test_tag/index.html | 140 +++++++++++++-- 46 files changed, 3949 insertions(+), 634 deletions(-) create mode 100644 android-chrome-192x192.png create mode 100644 android-chrome-384x384.png create mode 100644 android-chrome-512x512.png create mode 100644 apple-touch-icon.png create mode 100644 at.png create mode 100644 browserconfig.xml create mode 100644 d27124f241c74cecad2aabd9849a750e_1523816881.png create mode 100644 favicon-16x16.png create mode 100644 favicon-32x32.png create mode 100644 favicon.ico create mode 100644 mstile-150x150.png create mode 100644 safari-pinned-tab.svg create mode 100644 service-worker.min.js create mode 100644 site.webmanifest diff --git a/404.html b/404.html index b77d84a..b90521b 100644 --- a/404.html +++ b/404.html @@ -19,7 +19,7 @@ - +
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    + diff --git a/algolia.json b/algolia.json index 92244fb..d3dbc09 100644 --- a/algolia.json +++ b/algolia.json @@ -1 +1 @@ -[{"categories":["Agent"],"content":"将推理（Reson）和行动（Acting）相结合，使得语言模型能够处理多种语言推理和决策任务。","date":"2023-10-03","objectID":"/posts/react/","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"ReAct: 语言模型中推理和行为的协同作用 虽然大型语言模型（LLM）在语言理解和交互式决策方面的任务中表现出印象深刻的表现，但推理能力（例如思维链提示）和动作能力（例如动作规划生成）主要作为单独的主题进行研究。本文探讨LLM的使用，以插入的方式生成推理跟踪和特定任务的动作，从而在两者之间实现更大的协同：推理跟踪有助于模型诱导、跟踪和更新动作规划并处理异常，而动作与外部来源（如知识库或环境）交互并收集其他信息。作者取名ReAct的方法应用于各种语言和决策任务，证明其除了提高可解释性和可信度以外，还在有效性方面超越SOTA基线方法。具体来说，在问答（HotpotQA）和事实验证（Fever）上，ReAct通过与简单的维基百科API交互，克服了思维链推理中常见的幻觉和错误传播问题，并生成类似人类的任务解决轨迹，比没有推理痕迹的基线更具可解释性。此外，在两个交互式决策基准（ALFWorld和WebShop）上，ReAct的性能优于模仿和强化学习方法，绝对成功率分别为34%和10%，其中只有一两个上下文示例提示。 如图所示：（1）比较4种提示方法，(a).标准方法、(b).思维链（CoT，仅推理）、(c).仅动作、(d).ReAct（推理+动作）等，解决HotpotQA问题；（2） 比较2个提示方法，(a). 仅动作、和 (b). ReAct，去解决 一个AlfWorld 游戏。在这两个方法中，省略提示中的上下文示例，仅显示模型（Act，Thought）和环境（Obs）生成的任务解决轨迹。 ","date":"2023-10-03","objectID":"/posts/react/:1:0","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"1 简介 此前LLM的推理能力(如思维链提示)和行动能力(如行动计划生成) 是作为单独的主题进行研究的。 本文探索了使用LLM以交错的方式生成推理轨迹和用于特定任务的动作，允许两者之间有更大的协同作用。 在问答（HotpotQA）和事实验证（Fever）方面，ReAct通过与简单的维基百科API交互，克服了思维链推理中普遍存在的幻觉和错误传播问题，并产生了类似人类的任务解决轨迹，比没有推理轨迹的基线更容易解释。 ","date":"2023-10-03","objectID":"/posts/react/:1:1","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"2 思路 人类智能的一个独特特征是能够将任务导向的行为与言语推理无缝结合，这在人类认知中起着重要作用，可以实现自我调节或策略化。 以在厨房里烹饪一道菜为例。在任何两个具体动作之间，我们可以用语言进行推理，以跟踪进度： “现在所有的东西都切好了，我应该把锅里的水加热”， 根据情况处理异常或调整计划： “我没有盐，那就让我用酱油和胡椒代替吧”， 并意识到什么时候需要外部信息： “我怎么准备面团?让我上网搜索一下”。 我们也可以采取行动： 打开烹饪书阅读食谱，打开冰箱，检查食材 来支持推理和回答问题： “我现在可以做什么菜?”。 这种“表演”和“推理”之间的紧密协同，即使是在看不见的情况下或面临信息不确定性的情况下，也能使人类快速学习新任务，并执行稳健的决策或推理。 ","date":"2023-10-03","objectID":"/posts/react/:1:2","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"3 方法 ReAct提示大型语言模型为给定任务生成口头推理历史步骤和操作。这些提示由少量的上下文示例组成，这些示例指导模型的思考和操作生成。下面的图中给出了一个上下文示例。这些例子引导代理经历一个循环过程:产生一个想法，采取一个行动，然后观察行动的结果。通过结合推理跟踪和操作，ReAct允许模型执行动态推理，这样可以生成高级计划，还可以与外部环境交互以收集额外的信息。 ","date":"2023-10-03","objectID":"/posts/react/:1:3","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"4 相关工作 针对强调推理的任务，ReAct会交替生成推理路径和动作，形成多个“推理-动作-观测”步骤。与之相反，对于偏重决策的任务，可能只在有限的步骤内进行推理，因此推理路径在提示中会呈现出稀疏的模式，并由语言模型自主决定是否以异步方式进行推理和动作。 推理与动作之间的协同作用使模型能够通过状态推理来建立、维护和调整动作计划（推理→动作），同时还能够与外部环境（如维基百科）进行互动，在推理过程中参考额外信息（动作→推理）。 使用LLM进行推理的最著名的工作是思维链(Chain-of-Thought，CoT)。它揭示了LLM制定自己的“思维过程”来解决问题的能力。 此后进行了几项后续工作，包括解决复杂任务的最小到最大（least-to-most）提示，zero-shotCoT(不需要任何训练数据,只需要输入问题,模型就可以生成解决这个新问题的CoT思维链)和自一致性推理（ self-consistency）()。 最近，Madaan \u0026 Yazdanbakhsh 系统地研究了CoT的形成和结构，并观察到符号、模式和文本的存在对CoT的有效性至关重要。其他工作也被扩展到更复杂的推理架构，而不是简单的提示。 与这些方法相比，ReAct执行的不仅仅是孤立的、固定的推理，还将模型操作及其相应的观察集成到一个连贯的输入流中，以便模型更准确地推理并处理推理之外的任务(例如交互式决策)。 ","date":"2023-10-03","objectID":"/posts/react/:1:4","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"4 应用及结果 研究人员将ReAct应用于多种语言推理和决策任务，包括问题回答、事实验证、基于文本的游戏和网页导航。结果是非常好的，ReAct在可解释性和可信赖性方面始终优于其他最先进的基线。 在问答和事实验证任务中，ReAct通过与简单的Wikipedia API交互，克服了推理中普遍存在的幻觉和错误传播问题。它生成了类似人类的解决任务的步骤，比没有推理痕迹的基线更容易解释。在交互式决策基准中，ReAct的表现明显优于模仿和强化学习方法，即使只有一两个上下文示例。 虽然推理、行动和观察步骤交织在一起提高了ReAct的接地性和可信度，但这种结构也约束降低了其制定推理步骤的灵活性，导致在某些任务上的推理错误率高于思维链提示。 ","date":"2023-10-03","objectID":"/posts/react/:1:5","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"5 结论 ReAct是一种简洁而高效的方法，能够在语言模型中协同推理和行动。它证明了将模型的推理能力、动作生成以及与外部环境的反馈整合到语言模型中是可行的。这也为语言模型在处理涉及与外部环境交互的任务时充当代理提供了机会。 ","date":"2023-10-03","objectID":"/posts/react/:1:6","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"6 引用 REACT: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS Thanks for reading!","date":"2023-10-03","objectID":"/posts/react/:1:7","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Emergent Abilities"],"content":"量变引起质变：涌现的能力指在小模型时并不具有，表现接近随机，而在模型规模越过一个阈值时，表现突然提升","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"大语言模型涌现的新能力 语言模型是根据已知文本生成未知文本的模型。自GPT-3以来，大型语言模型展现出了惊人的zero-shot和few-shot能力，即不改变参数仅改变输入的in-context learning。这是与此前流行的finetune范式截然不同的新范式。近期的ChatGPT，更是让文本生成从以前人们眼中的玩具，逐渐展现出了生产力工具的潜质。 本文展现了语言模型的增大（scale up）并非只是提高了一两个点的指标，而是发生了质变，突变式地拥有了小语言模型所不具有的新能力。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:0","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"1 涌现 涌现（emergence）或称创发、突现、呈展、演生，是一种现象，为许多小实体相互作用后产生了大实体，而这个大实体展现了组成它的小实体所不具有的特性。 水分子聚集后组成了雪花是一个物理上的创发现象 涌现可以用一句经典的话来概括：量变引起质变。 智能就是一种涌现现象，我们很了解单个神经元的电生理反应，但智能并不蕴含于单个神经元之中。而当大量的神经元相互交互，“智能”却在其中产生了。整体并不简单等于部分之和，其会具有组分所不具有的新功能。涌现有时是很难解释的，毕竟如果我们能解释清楚神经元是如何涌现出的智能，人工智能也就完成大半了。 具体到语言模型上，涌现的能力指在小模型时并不具有，表现接近随机，而在模型规模越过一个阈值时，表现突然提升。 扩大（Scaling up）语言模型已被证明可以预测性地提高各种下游任务的性能和样本效率。 样本效率（Sample efficiency）是指学习算法在使用尽可能少的训练样本的情况下，在某个任务上获得良好表现的能力。换句话说，它衡量了算法在学习任务时需要多少数据才能有效地学习。 一个样本效率高的算法可以使用较少的样本学习与一个样本效率低的算法相同的任务。这在获取更多的训练数据可能很困难或昂贵的情况下尤为重要，例如医学诊断或机器人领域。 样本效率受多种因素的影响，包括任务的复杂性、训练数据的质量和相关性以及算法的设计和容量。一些学习算法由于其能够从有限的数据中很好地推广，因此比其他算法更具有样本效率；而其他算法需要更多的数据才能获得良好的性能。 因此，在评估和比较不同的学习算法时，样本效率是一个重要的指标。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:1","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"2 大语言模型的新能力 为了观察新能力是如何在模型规模跨过阈值时产生的，首先我们需要有衡量模型规模的指标：使用训练FLOPs和模型参数量是很自然的想法。需要注意的是，我们无法用一个指标来衡量所有影响模型的因素。两个模型可能有同样的FLOPs，但有不同的模型参数量（如MoE）。此外，数据集的质量，优化的好坏也会影响模型的表现。这使得我们无法得到一个具体的涌现发生的规模阈值。 论文关注的涌现能力是模型的prompting（in-context learning）能力，这是大型语言模型最核心的能力。Prompting是指不需要通过训练改变模型参数，仅需在输入中添加文本（如对任务的描述），使模型在此基础上补充回答。一句话概括结论：prompting相关的能力是随着模型规模的增大而涌现的。下面将从few-shot prompting和augmented prompting strategies两方面进行介绍。 2.1 Few-shot Prompting Few-shot prompting是给出若干个输入-输出对作为prompt，模型需对新输入补充输出。这可能是目前最常见的prompting方法。 如下图所示，许多任务随着模型规模跨过阈值而可以被few-shot prompting解决，包括：加减乘除（A），基于知识的问答（G）等。有趣的一点是，在以训练FLOPs为规模指标时，各模型、各任务的涌现阈值均在 1022 量级左右。这就像生物只有大脑足够大才能有高级智能一样，许多高级能力只有在模型达到一定规模时才能获得。 2.2 Augmented Prompting Strategies 除了few-shot prompting以外，还有其他prompting或者finetuning策略可以进一步增加语言模型的能力。比如说对于涉及多步推理和计算的任务，如果让语言模型直接生成答案，往往效果不佳。但通过prompting时给出逐步思考的范例（chain of thought），抑或finetune模型来预测中间过程（scratchpad），都能大幅提高模型表现。 如下图所示，augmented prompting strategies同样只在模型规模跨过一定阈值后起正面作用，对于小模型甚至起负面作用。 大型语言模型的涌现能力列表，以及这些能力出现的规模： ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:2","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"3 关于Scaling的思考 3.1 更大 BIG-Bench是一个包含超过200个任务的用于评测语言模型的数据集，其中并非所有的任务都会发生涌现（如下图所示）。有的任务表现随scale up而平滑增加，而有的任务至今为止还没有通过scale up超过随机表现。比如同为算术类任务，simple arithmetic会平滑增加，modified arithmetic发生了涌现，multistep arithmetic还没有超过随机表现。 这些未解决的任务会是进一步研究的对象。这些问题是否能单纯靠scale up解决？涌现的原因是什么？涌现之后scale up是否有性能上限？即使scale up在性能提升上不会遇到瓶颈，计算的负载也会成为巨大的问题。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:3","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"3.2 更小 影响模型能力的不仅有模型的规模，还有数据、模型结构或是训练方法。在更好的数据、模型结构、训练方法下，我们可以在同样甚至更小的模型规模下实现更好的效果。 使用更好的数据可以在更低的模型规模下实现涌现：如PaLM 62B以更少的模型参数与更低的FLOPs，突破了LaMDA 137B和GPT-3 175B只能取得随机表现的多个任务。虽然因为高昂的训练成本，不可能进行详尽的消融实验，但一个可能的原因是PaLM使用了更好的数据（如多语言数据、代码数据）。数据本身可能也是涌现的原因，如数据中的长程依赖、稀有类别与few-shot prompting的涌现有关，chain of thought能力可能来自于代码数据。 更好的模型结构也可以降低涌现的阈值。如encoder-decoder模型要更适合于instruction finetuning。对于decoder模型，instruction finetuning仅对68B以上参数量的模型有效。但encoder-decoder模型仅需要11B。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:4","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"4 Discussion 我们已经看到，在少量样本提示设置或其他情况下，一系列能力到目前为止只在对足够大的语言模型进行评估时才被观察到。因此，它们的出现不能仅通过小型模型的表现简单外推来预测。具有涌现能力的少量样本提示任务也是不可预测的，因为这些任务并没有在预训练中显式包含，而且我们可能不知道语言模型可以执行的少量样本提示任务的全部范围。 这引发了一个问题，即进一步扩展是否会赋予更大的语言模型新的涌现能力。语言模型目前无法完成的任务是未来出现的主要候选对象；例如，在 BIG-Bench 中有数十个任务，即使是最大的 GPT-3 和 PaLM 模型也无法实现高于随机的性能。 BIG-Bench 是一个评估语言模型能力的广泛基准（benchmark），由 AI2、微软和卡内基梅隆大学等机构合作开发。它涵盖了来自多个领域的70个任务，包括自然语言理解、常识推理、知识库问答等等。这些任务旨在测试语言模型在大规模、复杂、多样化的应用场景下的表现，是目前最具挑战性的语言模型测试集之一。BIG-Bench 的任务数量和难度要远高于其他常见的语言模型基准，它的推出对于评估和推动语言模型的发展具有重要意义。 模型大小并不是解锁涌现能力的唯一因素。随着训练大型语言模型的科学的进步，对于具有新体系结构、更高质量数据或改进的训练过程的较小模型，某些能力可能会被解锁。一个例子是，InstructGPT、ChatGPT、GPT-4 模型提出了一种基于人类反馈的微调和强化学习方法（RLHF），这使得一个参数量 1.3B 的模型在广泛的用例中，在人类评估方面的表现优于更大的模型。 重要的是，风险也可能会出现，例如，大型语言模型的社会风险，如真实性、偏见和毒性。无论它们是否可以准确地被描述为“涌现”，这些风险都是重要的考虑因素，并且在某些情况下，随着模型规模的增加而增加。由于关于涌现能力的工作鼓励语言模型的规模扩大，因此重要的是要意识到随着模型规模的增加而增加的风险，即使它们不是涌现的。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:5","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"5 Conclusions 我们已经讨论了语言模型的涌现能力，迄今为止，只有在一定的计算规模上才观察到有意义的表现。涌现能力可以跨越各种语言模型、任务类型和实验场景。这些能力是最近发现的大型语言模型的结果，它们是如何出现的，以及更多的扩展是否会出现进一步的涌现能力成为 NLP 领域未来重要的研究方向。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:6","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"6 引用 Emergent Abilities of Large Language Models Thanks for reading!","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:7","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Agent"],"content":"LLM+P将传统规划方法的优点整合到LLMs的框架中","date":"2023-09-05","objectID":"/posts/llm+p/","tags":["LLM"],"title":"【论文阅读】LLM+P","uri":"/posts/llm+p/"},{"categories":["Agent"],"content":"LLM+P：赋予大型语言模型规划能力 大语言模型(LLMs)已经展示了显著的零样本泛化能力，最先进的聊天机器人能够对日常生活中的常见问题给出合理的回答。然而，目前为止，LLMs并未能可靠地解决长期规划问题。相反，一旦以一种规范的方式给出问题，传统的规划方法就能够运用有效的搜索算法快速找到正确的，甚至是最优的解决方案。为了把这两者的优势结合起来，本文提出了LLM+P，这是第一个将传统规划方法的优点整合到LLMs的框架中。LLM+P接收一个用自然语言描述的规划问题，然后返回一个以自然语言表述的，解决该问题的正确（或最优）方案。LLM+P首先将语言描述转化为一个用规划领域定义语言（PDDL）编写的文件，然后利用传统规划方法快速找到解决方案，最后将找到的解决方案翻译回自然语言。此外，我们还定义了一组取自常见规划场景的基准问题。通过在这些基准问题上进行全面的实验，我们发现LLM+P能够为大多数问题提供最优解决方案，而LLMs在大多数问题上却无法提供可行的方案。 ","date":"2023-09-05","objectID":"/posts/llm+p/:1:0","tags":["LLM"],"title":"【论文阅读】LLM+P","uri":"/posts/llm+p/"},{"categories":["Agent"],"content":"1 PDDL介绍 Planning Domain Definition Language (PDDL) 是一种用于描述人工智能规划问题的语言。它包括领域定义和问题定义两个部分。 领域定义描述了可能的动作和它们的效果，每个动作都有一些前提条件，这些条件必须满足才能执行该动作，以及每个动作的效果，描述了动作执行后世界的状态如何改变。 问题定义描述了一个具体的规划问题，包括初始状态和目标状态。初始状态描述了规划问题开始时世界的状态，而目标状态描述了我们希望达到的状态。 例如，一个简单的\"搬箱子\"问题可以这样描述： 领域定义： (define (domain move) (:requirements :strips) (:predicates (robot-at ?x) (box-at ?x) (goal-at ?x)) (:action move-robot :parameters (?from ?to) :precondition (robot-at ?from) :effect (and (not (robot-at ?from)) (robot-at ?to))) (:action move-box :parameters (?from ?to) :precondition (and (robot-at ?from) (box-at ?from)) :effect (and (not (box-at ?from)) (box-at ?to) (not (robot-at ?from)) (robot-at ?to))) ) 问题定义： (define (problem move-1) (:domain move) (:objects location1 location2 location3) (:init (robot-at location1) (box-at location2) (goal-at location3)) (:goal (and (box-at location3))) ) 这个例子中，机器人和箱子可以在不同位置移动，目标是将箱子移动到目标位置。 然后，我们可以使用规划器，如 Fast Downward，来求解这个问题。Fast Downward 是一个非常知名的规划器，它使用了一种名为\"因果图\"的数据结构来帮助搜索解决方案。Fast Downward 接受 PDDL 描述的问题作为输入，然后使用各种搜索策略和启发式方法来寻找一个解决方案，即一系列的动作序列，这些动作将系统从初始状态引导到目标状态。 PDDL的优点是可扩展性强，但处理连续动作或状态、不确定效果或不完全信息的能力有限。 ","date":"2023-09-05","objectID":"/posts/llm+p/:1:1","tags":["LLM"],"title":"【论文阅读】LLM+P","uri":"/posts/llm+p/"},{"categories":["Agent"],"content":"2 文章提出的方法 本节首先使用一个实际的例子探讨如何以PDDL的形式构建规划提示，然后介绍LLM+P方法。 2.1 用LLM写PDDL 虽然LLMs在规划（或者说长期推理）方面表现得不尽人意，但它们在描述和翻译文本输入方面，包括以PDDL格式重写规划提示，方面做得很好。直观上，我们可以将PDDL视为一种不同于英语的语言，所以将规划提示以PDDL的形式重写其实就是一项“机器翻译”的任务，这是LLMs擅长的领域。这里我们提供了一个由GPT-4写就，没有进行任何优化的PDDL问题文件示例。我们可以看到，生成的文件似乎遵循了正确的PDDL语法，但使用了一个虚构的谓词（empty），并且漏掉了b1在桌子上的初始条件。 2.2 上下文学习 LLMs已经展示出了在不进行微调的情况下进行上下文学习的能力。上下文学习，指的是LLMs仅通过对一些输入-标签对（示例）的条件化，就能执行未见过的下游任务的能力。这里我们提供了一对用自然语言和PDDL描述的问题，作为GPT-4的上下文。当我们将这个上下文与上述示例中的提示结合起来时，生成的PDDL问题文件可以直接由规划器解决。 2.3 大型语言模型 + 传统规划方法 (LLM+P) 在介绍了LLM将问题以PDDL编码和上下文学习的能力后，我们现在准备介绍所提出的LLM+P解决方案（见图1的底部）。具体来说，我们假设对于每个问题领域，一个人类专家可以提供一个领域描述（也就是说，动作的先决条件和效果），这个描述将对该领域中出现的所有问题实例起到固定的作用。虽然自动生成这个描述的问题是另一个有价值的研究课题，但在这项提出的工作中，我们假设这个描述是以一个PDDL领域文件的形式存在的。LLM+P方法可以直接应用为给机器人系统指定任务的自然语言接口。例如，假设我们想要一个机器人扮演酒保的角色来制作鸡尾酒。告诉它可以执行哪些动作是合理的，但让它自己推断如何最有效地利用一组要组合的配料来制作新的鸡尾酒。此外，我们假设该代理人提供了一个最小的示例，演示了在该领域中一个简单问题的PDDL形式是什么样的。接下来，代理人提供了一个新的（可能非常复杂的）问题（P）。然后，LLM利用上下文学习来推断出与P对应的PDDL问题文件。一旦生成了问题的PDDL文件，我们将其与提供的领域PDDL文件一起输入到任何经典的规划方法中，生成一个PDDL方案。最后，LLM将PDDL方案翻译回自然语言，完成了LLM+P的流程。 总结来说，LLM+P的假设是： 聊天机器人能够根据与人类用户的对话，知道何时触发LLM+P。 对于用户提出的问题，提供了一个领域的PDDL文件。 提供了一个简单问题的自然语言描述和其对应的PDDL文件。 ","date":"2023-09-05","objectID":"/posts/llm+p/:1:2","tags":["LLM"],"title":"【论文阅读】LLM+P","uri":"/posts/llm+p/"},{"categories":["Agent"],"content":"3 引用 LLM+P: Empowering Large Language Models with Optimal Planning Proficiency Thanks for reading!","date":"2023-09-05","objectID":"/posts/llm+p/:1:3","tags":["LLM"],"title":"【论文阅读】LLM+P","uri":"/posts/llm+p/"},{"categories":["一些奇怪的东西"],"content":"0行代码、1.5元成本，基于全世界最先进的对话ai模型ChatGPT搭建属于你自己的微信对话机器人！","date":"2022-12-12","objectID":"/posts/chatgpt/","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"0 写在前面 ChatGPT（Conversational Graph-based Pre-trained Transformer）是一种面向对话机器人的预训练Transformer模型，旨在通过训练海量的对话数据来提高机器人的语言理解能力。这是一种基于图模型的预训练Transformer，可以更好地理解上下文和句子之间的关系，并自动学习语言中的模式，以更好地回答用户的问题。 关于ChatGPT能做什么，参考：ChatGPT惊人语录大赏 ","date":"2022-12-12","objectID":"/posts/chatgpt/:1:0","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"0.0 准备材料 电脑 梯子 1.5元 ","date":"2022-12-12","objectID":"/posts/chatgpt/:1:1","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"0.1 最终效果 实现一个基于ChatGPT的微信聊天机器人，支持单人私聊和拉入微信群@它聊天，若是在本地电脑运行则电脑关机会导致机器人下线，运行在云服务器则能一直使用。 不想折腾？直接添加我制作的微信机器人：somewhom1 ","date":"2022-12-12","objectID":"/posts/chatgpt/:1:2","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"0.2 注意事项 部署完chatgpt到某一个微信后，不能在手机退出这个微信账号，否则会失效； 若要切换部署到其他微信账号，需要将扫码后 config.json 所在目录下生成的 storage.json 文件删除再重新运行扫码。 部署chatgpt的可执行程序不可关闭，否则机器人将失效，若想不间断服务，建议在云服务器上后台运行。 ","date":"2022-12-12","objectID":"/posts/chatgpt/:1:3","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"1 注册OpenAI账号 ","date":"2022-12-12","objectID":"/posts/chatgpt/:2:0","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"1.0 OpenAI官网 挂梯子，进入OpenAI官网，点击右上角SIGN UP，输入邮箱注册或通过谷歌或微软账号注册，直到输入手机号验证（由于OpenAI在中国不提供服务，需要一个国外的手机号接收验证码验证）。 ","date":"2022-12-12","objectID":"/posts/chatgpt/:2:1","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"1.1 SMS-Activate接收国外手机号验证码 挂梯子，进入SMS-Activate官网，注册账号，邮箱验证有点抽风，需要等一下。 账号注册好后在网页左侧搜索openai： 点击后可以看到最便宜的印度和印度尼西亚号码只需要7卢布（不到人民币1元）： 不过我建议租7.5卢布的马来西亚号码，反正我自己租印度号码没收到验证码。购买需要充值大约0.15美元： 大约是1.2元不到： 在openai注册时需要的手机号验证中填写所租号码的地区号和手机号进行验证，过一会儿会在sms-activate中收到验证码，如果收不到在20分钟内可以取消该手机号重新租一个： 填写收到的验证码，完成注册。 至此你可以登录openai在线试用它提供的所有ai服务，除了ChatGPT外还有ai绘画、文本生成、代码生成等，这些也很有趣。如果想在线体验ChatGPT，可以点击上方的 Try it 按钮。 ","date":"2022-12-12","objectID":"/posts/chatgpt/:2:2","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"2 部署ChatGPT到微信 ","date":"2022-12-12","objectID":"/posts/chatgpt/:3:0","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"2.0 简要介绍 本部署方式基于github上的一个开源项目，原作者代码仓：https://github.com/869413421/wechatbot 某位博主上传到gitee（不需梯子）的版本：https://gitee.com/shtml/wechatbot 项目基于openwechat 开源项目开发，代码仓：https://github.com/eatmoreapple/openwechat 作者提到的部署方式有两种： 支持Windows 和 Linux，但是本机需要有Golang环境。直接编译运行项目即可。 支持Windows 和 Linux，不需本地有Golang环境。下载可执行文件直接启动即可。简单无脑，下面的教程基于这种部署方式。 ","date":"2022-12-12","objectID":"/posts/chatgpt/:3:1","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"2.1 部署到本地 2.1.0 下载所需文件 有git的情况 进入准备安装的目录 git clone https://gitee.com/shtml/wechatbot.git 没git的情况 进入https://gitee.com/shtml/wechatbot/tree/main/bin下载config.json和一个可执行文件（这两个文件要放在同一个目录下），可执行文件参考电脑系统，386为32位系统，amd64为64位系统，.exe为windows系统的可执行文件，linux结尾为linux系统，64位windows系统下载wechatbot-amd64.exe。 2.1.1 配置config.json 挂梯子，再次进入https://beta.openai.com/overview，登录openai账号后，点击右上角账号： 再点击 View API Keys ，第一次需要点击Create new secret key创建API Keys： 创建好后复制这个key，进入之前存放config.json文件的目录进行配置： 可直接用记事本打开config.json，把apikey的内容改为之前复制的 API key ： auto_pass字段推荐改为 false ，这个字段为是否自动同意好友申请。 至此，配置完成。 2.1.2 运行程序 运行之前下载的可执行文件 wechatbot-amd64.exe： 若浏览器没有自动跳转就将下面的网址复制到浏览器打开，会出现一个二维码，用微信扫描完成登录，此时可执行文件所在目录会出现一个 storage.json文件 。 至此，微信对话机器人部署完成，你拥有了一个目前最先进的对话机器人，try it！ ","date":"2022-12-12","objectID":"/posts/chatgpt/:3:2","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"2.2 部署到云服务器 注：上述过程已经完成了一次在本地的部署，可供短期玩耍，但当本地电脑关机或程序关闭时机器人会失效，部署在云服务器上可解决这一问题，但这种方式需要一些简单的技术功底。 2.2.0 租云服务器 这里推荐阿里云服务器，新用户注册可以免费使用一个月：阿里云新人特惠链接 反正免费，推荐能选的配置越高越好，还可以折腾折腾别的，推荐租 ubuntu 系统的云服务器。 2.2.1 部署chatgpt到云服务器 进入用户目录： cd /home 拉取代码： git clone https://gitee.com/shtml/wechatbot.git 进入bin目录： cd wechatbot/bin 编辑config.json（配置与2.1.1相同）： vim config.json 创建screen会话： screen -S wechatbot 运行可执行文件： ./wechatbot-amd64-linux 将命令行中的链接复制到浏览器扫码登录，最后 Ctrl + a + d 将会话放于后台运行。 至此，云服务器上的ChatGPT微信机器人部署完成，你拥有了一个24小时工作的目前最先进的对话机器人，have fun！ 若登录后要切换微信，则需要删除 storage.json 文件： rm storage.json ","date":"2022-12-12","objectID":"/posts/chatgpt/:3:3","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"3 引用 OpenAI ChatGPT 接入 个人WeChat(微信)，让AI互动更方便！ Thanks for reading!","date":"2022-12-12","objectID":"/posts/chatgpt/:4:0","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["Study Blog"],"content":"description","date":"2022-12-07","objectID":"/posts/hpe/","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"1 任务介绍 3D Human Pose Estimation（以下简称 3D HPE ）的目标是在三维空间中估计人体关键点的位置。3D HPE 的应用非常广泛，包括人机交互、运动分析、康复训练等，它也可以为其他计算机视觉任务（例如行为识别）提供 skeleton 等方面的信息。 近几年，随着深度学习在人体姿态估计领域的成功应用，2D HPE 的精度和泛化能力都得到了显著提升。然而，相较于 2D HPE，3D HPE 面临着更多的挑战。一方面，受数据采集难度的限制，目前大多数方法都是基于单目图像或视频的，而从 2D 图像到 3D 姿态的映射本就是一个多解问题。另一方面，深度学习算法依赖于大量的训练数据，但由于 3D 姿态标注的难度和成本都比较高，目前的主流数据集基本都是在实验室环境下采集的，这势必会影响到算法在户外数据上的泛化性能。另外，2D HPE 面临的一些难题（例如自遮挡）同样也是 3D HPE 亟待解决的问题。 ","date":"2022-12-07","objectID":"/posts/hpe/:1:0","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"2 表示方式 骨架关键点：以骨架的形式表示人体姿态，由一系列的人体关键点和关键点之间的连线构成； 参数化的人体模型：以mesh（网格）形式表示人体姿态和体型。 ","date":"2022-12-07","objectID":"/posts/hpe/:2:0","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"3 多人场景 多人场景的3D-HPE和2D-HPE类似，可分为top-down（自顶向下）和bottom-up（自底向上）两类。 top-down top-down 方法先利用目标检测算法确定人体的 bounding box，然后对于每一个 bounding box 中的人体，计算其根关节的绝对坐标以及其他关节相对于根关节的坐标。 bottom-up bottom-up 方法先预测所有关节的位置，再根据关节间的相对关系将属于同一个人的关节联系起来构成完整的人体。 Bottom-up 方法的主要优点在于它的运行时间基本不会受到待检测人体数量的影响，因此在拥挤场景下 bottom-up 方法更有优势。再者，bottom-up 方法的关键点定位是在整体图像的基础上完成的，而 top-down 方法则是在 bounding box 区域内完成的，因此 bottom-up 方法更有利于把握全局信息，从而能更准确地定位人体在相机坐标系下的绝对位置。 ","date":"2022-12-07","objectID":"/posts/hpe/:3:0","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"4 常用数据集 由于 3D 关键点标注难度较大，目前的数据集基本上都借助于 MoCap 和可穿戴 IMU设备来完成数据标注，也正因为此，大多数数据集都局限于室内场景。 ","date":"2022-12-07","objectID":"/posts/hpe/:4:0","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"4.1 Human3.6M Human3.6M 是目前基于多视图的 3D 人体位姿研究最为重要的一个数据集，包含了360万帧图像和对应的 2D/3D 人体姿态。该数据集在实验室环境下采集，通过4个高清相机同步记录4个视角下的场景，并通过 MoCap 系统获取精确的人体三维关键点坐标及关节角。如图所示，Human3.6M 包含了多样化的表演者、动作和视角。 Human3.6M 的评价指标主要有 Mean Per Joint Position Error (MPJPE) 和 P-MPJPE。其中，MPJPE 是所有关键点预测坐标与 ground truth 坐标之间的平均欧式距离，一般会事先将预测结果与 ground truth 的根关节对齐；P-MPJPE 则是将预测结果通过 Procrustes Analysis 与 ground truth 对齐，再计算 MPJPE。 ","date":"2022-12-07","objectID":"/posts/hpe/:4:1","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"4.2 MPI-INF-3DHP Human3.6M 尽管数据量大，但场景单一。为了解决这一问题，MPI-INF-3DHP 在数据集中加入了针对前景和背景的数据增强处理。具体来说，其训练集的采集是借助于多目相机在室内绿幕场景下完成的，对于原始的采集数据，先对人体、背景等进行分割，然后用不同的纹理替换背景和前景，从而达到数据增强的目的。测试集则涵盖了三种不同的场景，包括室内绿幕场景、普通室内场景和室外场景。因此，MPI-INF-3DHP 更有助于评估算法的泛化能力。 在评价标准方面，除了 MPJPE，该数据集也把 2D HPE 中广泛使用的 Percentage of Correct Keypoints (PCK) 和 Area Under the Curve (AUC) 扩展到了 3D HPE 中。其中 PCK 是误差小于一定阈值（150 mm）的关键点所占的百分比，AUC 则是 PCK 曲线下的面积。 ","date":"2022-12-07","objectID":"/posts/hpe/:4:2","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"4.3 Panoptic (CMU Panoptic Studio) CMU Panoptic [13] 是一个大型的多目图像数据集，提供了31个不同视角的高清图像以及10个不同视角的 Kinect 数据，包含了65段视频（总时长5.5 h），3D skeleton 总数超过150万。该数据集还包含了多个多人场景，因此也成为了多人 3D HPE 的 benchmark 之一。 CMU Panoptic 的评价标准与 Human3.6M 类似，一般采用 MPJPE 和 P-MPJPE 来评估算法精度。 ","date":"2022-12-07","objectID":"/posts/hpe/:4:3","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"5 主流算法 ","date":"2022-12-07","objectID":"/posts/hpe/:5:0","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"5.1 基于单目图像的方法 由于单目图像易于获取且不受场景限制，很多方法都以此为输入数据。但是根据 2D 图像估计 3D 姿态是一个不适定问题，即可能存在多个不同的 3D 姿态，它们的 2D 投影是同一个 2D 姿态。并且，基于单目图像的方法也面临着自遮挡、物体遮挡、深度的不确定性等问题。由于缺少 3D 信息，目前的方法大多只能预测 root-relative pose，即以根关节（pelvis）为坐标原点的三维姿态。 基于单目图像的方法可以根据是否依赖 2D HPE 分成两类，下面将分别介绍这两类方法。 5.1.1 直接预测 这类方法不依赖 2D HPE，直接从图像回归得到 3D 关键点坐标。代表作 C2F-Vol [3] 借鉴了 2D HPE 中的 Hourglass 网络结构，并以 3D Heatmap 的形式表示 3D pose。为了降低三维数据带来的巨大存储消耗，采用了在 depth 维度上逐渐提升分辨率的方法。 5.1.2 2D-to-3D Lifting 得益于 2D HPE 的高精度和泛化能力，许多方法选择以 2D HPE 作为中间步骤，根据 2D pose（和原始图像特征）去估计 3D pose。SimpleBaseline3D [4] 是其中的一种经典方法。该方法以 2D 关键点坐标作为输入，通过残差连接的全连接层直接将 2D pose 映射到 3D 空间。尽管模型非常简单，该算法在当时达到了 SOTA 水平，并通过实验证明了目前大多数 3D HPE 算法的误差主要来源于图像信息的理解（2D HPE）而不是 2D-to-3D lifting 过程。 由于上述算法仅以 2D pose 作为输入，因此高度依赖 2D pose 的准确性，一旦 2D HPE 失败，将会严重影响后续的 2D-to-3D lifting。为了解决这一问题，也有一些算法同时学习 2D 和 3D 姿态 [5]，这样一方面可以为 2D-to-3D lifting 引入来自于原始图像的信息，另一方面也为 2D/3D 数据集混合训练提供了可能，进一步提升算法的泛化能力。 ","date":"2022-12-07","objectID":"/posts/hpe/:5:1","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"5.2 基于多目图像的方法 为了解决遮挡问题，目前最有效的方法之一就是融合多视角信息。为了从多目图像中重建 3D pose，关键在于如何确定场景中同一个点在不同视角下的位置关系。一些方法引入多视角间的一致性约束，例如下图的方法：同时输入两个视角的图像，对于其中某一个视角的 2D pose 输入，根据两个视角间的转换关系，预测另一个视角的 3D pose 输出。也有一些方法借助于立体视觉中的 triangulation 方法，将所有视角下的 2D heatmap 聚合起来构成一个 3D volume，随后再通过三维卷积网络输出 3D heatmap。 结合多目图像可以帮助解决遮挡问题，并且也能够在一定程度上解决深度不确定问题。但是这类方法对于数据采集的要求比较高，且模型结构相对复杂，在实际场景中的应用具有一定的局限性。 ","date":"2022-12-07","objectID":"/posts/hpe/:5:2","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"5.3 基于视频的方法 基于视频的方法就是在以上两类方法的基础上引入时间维度上的信息。相邻帧提供的上下文信息可以帮助我们更好地预测当前帧的姿态。对于遮挡情况，也可以根据前后几帧的姿态做一些合理推测。另外，由于在一段视频中同一个人的骨骼长度是不变的，因此这类方法通常会引入骨骼长度一致性的约束限制，有助于输出更加稳定的 3D pose。 ","date":"2022-12-07","objectID":"/posts/hpe/:5:3","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"5.4 其他方法 除了 RGB 图像以外，也有越来越多的传感器被应用于 3D HPE 任务中。常见的有深度相机、雷达、IMU（惯性测量单元）等。深度相机和雷达都提供了三维空间上的信息，这对于 3D HPE 来说非常重要，可以帮助缓解或消除深度不确定性问题。IMU 设备可以提供关节方向信息，它不依赖于视觉信号，因此可以帮助解决遮挡问题。 Thanks for reading!","date":"2022-12-07","objectID":"/posts/hpe/:5:4","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["tast_cate"],"content":"description","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":["tast_cate"],"content":"Self-Supervised PreTrain ","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/:0:0","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":["tast_cate"],"content":"Pretext task Pretext task也叫surrogate task，也称作代理任务。 Pretext可以理解为是一种为达到特定训练任务而设计的间接任务。比如，我们要训练一个网络来对ImageNet分类，可以表达为 fθ(x): x→y，我们的目的其实是获得具有语义特征提取/推理能力的 θ。我们假设有另外一个任务（也就是pretext），它可以近似获得这样的θ，比如，Auto-encoder（AE），表示为：gθ(x): x→x。为什么AE可以近似 θ呢？因为AE要重建 x 就必须学习 x 中的内在关系，而这种内在关系的学习又是有利于我们学习 fθ(x) 的。这种方式也叫做预训练，为了在目标任务上获得更好的泛化能力，一般还需要进行fine-tuning等操作。 ","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/:1:0","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":["tast_cate"],"content":"MAE Fine-tuning linear probe linear probe不对训练好的模型做变动，只是用它从下游数据集提取特征，然后利用逻辑回归拟合标签； fine-tune fine-tune利用下游数据再次微调预模型中的所有参数。 ","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/:2:0","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":["tast_cate"],"content":"CAE (a)CAE；(b)BEiT；(c)DAE encoder 仅用来做表征，将patches映射到一个表征空间。 latent contextual regressor 用来通过使用encoder表征的可视patches来预测masked patches的表征，并添加一个alignment constraint来与使用encoder表征的masked patches进行对齐。 这一部分的作用使得CAE的encoder承担了全部的表征学习能力。 decoder 将masked patches的表征映射回targets。 ","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/:3:0","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":["tast_cate"],"content":"Contrastive Learning 典型的对比学习的任务是通过解决最大化来自同一图像的增强视图之间的相似度以及最小化来自不同图像的增强图像之间的相似度来预训练一个网络。 对于ImageNet数据集，典型的对比学习主要学到了图像中心关于这一千个分类的知识，而MIM方法能够学到非中心区域的、这一千个分类以外的知识。 Thanks for reading! ","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/:4:0","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":null,"content":"FCN 简介 FCN（Fully Convolutional Networks，全卷积网络）是Jonathan Long等人于2015年在Fully Convolutional Networks for Semantic Segmentation一文中提出的用于图像语义分割的一种框架，是深度学习用于语义分割领域的开山之作。FCN将传统CNN后面的全连接层换成了卷积层，这样网络的输出将是热力图而非类别；同时，为解决卷积和池化导致图像尺寸的变小，使用上采样方式对图像尺寸进行恢复。 核心思想 不含全连接层的全卷积网络，可适应任意尺寸输入； 反卷积层增大图像尺寸，输出精细结果； 结合不同深度层结果的跳级结构，确保鲁棒性和精确性。 网络结构 FCN是一个端到端，像素对像素的全卷积网络，用于进行图像的语义分割。整体的网络结构分为两个部分：全卷积部分和上采样部分。FCN中，使用了vgg16的卷积部分作为backbone，并将vgg16的最后三个全连接层也改为卷积层。除此之外，还增加了上采样部分，这里是使用转置卷积进行上采样. 优缺点分析 优点 FCN网络可以实现端到端的预测，可以接受任意大小的输入图像尺寸（因为没有全连接层），比较高效。 局限性 得到的结果还是不够精细。进行8倍上采样虽然比32倍的效果好了很多，但是上采样的结果还是比较模糊的，对图像中的细节不敏感。而且在对各个像素进行分类时，没有考虑像素与像素之间的关系。 ","date":"2022-10-27","objectID":"/posts/fcn+ampamp+u-net/:1:0","tags":null,"title":"FCN \u0026 U-Net","uri":"/posts/fcn+ampamp+u-net/"},{"categories":null,"content":"U-Net 简介 U-Net最早发表在2015的MICCAI上。后成为大多做医疗影像语义分割任务的baseline，也启发了大量研究者去思考U型语义分割网络。在自然影像理解方面，越来越多的语义分割和目标检测SOTA模型开始关注和使用U型结构，比如语义分割Discriminative Feature Network(DFN)(CVPR2018)，目标检测Feature Pyramid Networks for Object Detection(FPN)(CVPR 2017)等。 网络结构 UNet 网络结构如上图所示，其网络结构是对称的，形似英文字母 U，故而被称为 UNet 。就整体而言，UNet 是一个Encoder-Decoder的结构（与 FCN 相同），前半部分是特征提取，后半部分是上采样。 Encoder：左半部分，由两个 3x3 的卷积层（ReLU）+ 一个 2x2 的 maxpooling 层组成一个下采样模块。由卷积操作和下采样操作组成，所用卷积结构统一为 3x3 的卷积核，padding=0，striding=1。没有 padding 所以每次卷积之后特征图的 H 和 W 变小了，在跳层连接（Skip connection）时需注意特征图的维度。 Decoder：右半部分，由一个上采样的卷积层 + 特征拼接 concat + 两个 3x3 的卷积层（ReLU）构成一个上采样模块。Decoder 用以恢复特征图的原始分辨率，除了卷积以外，该过程的关键步骤就是上采样与跳层连接。上采样常用转置卷积和插值两种方式实现。在插值实现方式中，双线性插值（bilinear）的综合表现较好也较为常见 。UNet 中的跳层连接通过拼接将底层的位置信息与深层的语义信息相融合。而在在 FCN 中，特征图是以相加的方式进行融合。 与FCN对比 FCN：通过特征图对应像素值的相加来融合特征。特征图维度没有变化，但每个维度包含了更多特征。对于普通分类任务这种不需要从特征图复原到原始分辨率的任务来说，这是一个高效的选择； UNet ：通过通道数的拼接，以形成更厚的特征（当然这样会更佳消耗显存）。拼接方式：保留了更多的维度和位置信息，这使得后面的网络层可在浅层特征与深层特征间自由选择，这对语义分割任务来说更具优势。 与FPN的对比 同： 都使用了“由上至下”、“横向连接”及“由下至上”的结构，从而对多尺度特征图进行融合，即将高层的语义信息与低层的几何细节结合。另外，融合后都会再经过一层卷积。 异： FPN对多尺度特征图融合的方式是element-wise add，而UNet采用的是concate； FPN对多尺度特征图都进行了预测，而UNet仅在（由上至下）最后一层进行预测，而且这一层通常还需要进行一次resize才能恢复到原图尺寸； FPN对高层特征图采用的放大方式是插值，而UNet通常还会使用转置卷积，通过网络自学习的方式来进行上采样； FPN的高层特征放大2倍后与低层的尺寸恰好一致，而在UNet中通常不一致，还需要对低层特征做crop使得与放大后的高层特征尺寸一致； FPN在下采样时的卷积带有padding，分辨率的下降仅由stirde决定，而UNet的卷积通常不带padding，使得分辨率下降在stride的基础上还会额外的减小。也就是说，FPN的“由下至上”和“由下至上”是对称结构，而UNet其实是非对称的，这也是导致4和2中最后提到的原因‘； FPN在特征层融合后经过一层卷积是为了消除上采样过程中产生的混叠效应带来的影响，而UNet中还起到了压缩通道的作用（也是由于UNet融合特征层时采用的是concate，因此需要压缩通道减少计算量）； FPN主要针对detection任务，而UNet针对segmentation任务，前者通常作为一个模块嵌入到网络结构中，而后者本身就是一种网络模型结构。 为什么适用于医学图像？ 因为医学图像边界模糊、梯度复杂，需要较多的高分辨率信息。高分辨率用于精准分割。 人体内部结构相对固定，分割目标在人体图像中的分布很具有规律，语义简单明确，低分辨率信息能够提供这一信息，用于目标物体的识别。 UNet结合了低分辨率信息（提供物体类别识别依据）和高分辨率信息（提供精准分割定位依据），完美适用于医学图像分割。 ","date":"2022-10-27","objectID":"/posts/fcn+ampamp+u-net/:2:0","tags":null,"title":"FCN \u0026 U-Net","uri":"/posts/fcn+ampamp+u-net/"},{"categories":null,"content":"Citation FCN网络解析 FCN（全卷积神经网络）详解 UNet 浅析 研习U-Net FPN与Unet的异同 Thanks for reading! ","date":"2022-10-27","objectID":"/posts/fcn+ampamp+u-net/:3:0","tags":null,"title":"FCN \u0026 U-Net","uri":"/posts/fcn+ampamp+u-net/"},{"categories":null,"content":"FPN 提出原因 卷积网络中，深层网络容易响应语义特征，浅层网络容易响应图像特征。然而，在目标检测中往往因为卷积网络的这个特征带来了不少麻烦：高层网络虽然能响应语义特征，但是由于Feature Map的尺寸太小，拥有的几何信息并不多，不利于目标的检测；浅层网络虽然包含比较多的几何信息，但是图像的语义特征并不多，不利于图像的分类。这个问题在小目标检测中更为突出。因此需要能够合并深层和浅层特征的网络，同时满足目标检测和图像分类的需要。 参考思想 FPN使用的是图像金字塔的思想。 传统的图像金字塔采用输入多尺度图像的方式构建多尺度的特征。输入一张图像后，可以通过一些手段获得多张不同尺度的图像，将这些不同尺度图像的4个顶点连接起来，就可以构造出一个类似真实金字塔的一个图像金字塔。整个过程有点像是我们看一个物品由远及近的过程（近大远小原理）。 其中，中间的图像是原始图像，尺寸越来越小的图片是经过下采样处理后的结果，而尺寸越来越大的图片是经过上采样处理后的结果。把高层的特征传下来，补充低层的语义，这样就可以获得高分辨率、强语义的特征，有利于小目标的检测。 特征金字塔 运用金字塔的思想可以提高算法的性能，但是需要大量的运算和内存。因此特征金字塔要在速度和准确率之间进行权衡，通过它获得更加鲁棒的语义信息。 图像中存在不同大小的目标，而不同的目标具有不同的特征，所以需要特征金字塔来利用浅层的特征将简单的目标区分开，利用深层的特征将复杂的目标区分开。即利用大的特征图区分简单目标，利用小的特征图区分复杂目标。 具体思路 图（a）： 先对原始图像构造图像金字塔，然后在图像金字塔的每一层提出不同的特征，然后进行相应的预测。 优点：精度较好。 缺点：计算量和占用内存太大。 图（b）： 通过对原始图像进行卷积和池化操作来获得不同尺寸的feature map，在图像的特征空间中构造出金字塔。因为浅层的网络更关注于细节信息，高层的网络更关注于语义信息，更有利于准确检测出目标，因此利用最后一个卷积层上的feature map来进行预测分类。 优点：速度快、内存少。 缺点：仅关注深层网络中最后一层的特征，却忽略了其它层的特征。 图（c）： 同时利用低层特征和高层特征。就是首先在原始图像上面进行深度卷积，然后分别在不同的特征层上面进行预测。 优点：在不同的层上面输出对应的目标，不需要经过所有的层才输出对应的目标（即对于有些目标来说，不用进行多余的前向操作），速度更快，又提高了算法的检测性能。 缺点：获得的特征不鲁棒，都是一些弱特征（因为很多的特征都是从较浅的层获得的）。 图（d）： FPN网络，对最底层的特征进行向上采样，并与该底层特征进行融合，得到高分辨率、强语义的特征（即加强了特征的提取）。 整体过程 自下而上：先把预处理好的图片送进预训练的网络，如ResNet，构建自下而上的网络，对应上图左侧金字塔。 自上而下：左侧顶层直接复制到右侧顶层，对右侧顶层进行上采样操作（就是2 * up），再用1 * 1卷积对左侧次顶层进行降维处理，然后将两者对应元素相加（这里就是高低层特征的一个汇总），后续以此类推，如此构成自上而下网络。 卷积融合：最后我们对右侧各层分别来一个3 * 3卷积操作得到最终的预测（对应上图的predict）。 ","date":"2022-10-26","objectID":"/posts/fpn+ampamp+ssd+ampamp+yolo/:1:0","tags":null,"title":"FPN \u0026 SSD \u0026 YOLO","uri":"/posts/fpn+ampamp+ssd+ampamp+yolo/"},{"categories":null,"content":"SSD 简介 SSD，全称Single Shot MultiBox Detector，是Wei Liu在ECCV 2016上提出的一种目标检测算法，截至目前是主要的检测框架之一，相比Faster RCNN有明显的速度优势，相比YOLO又有明显的mAP优势。 背景 目标检测主流算法分成两个类型： two-stage方法：RCNN系列 通过算法产生候选框，然后再对这些候选框进行分类和回归。 one-stage方法：yolo和SSD 直接通过主干网络给出类别位置信息，不需要区域生成。 特点 从YOLO中继承了将detection转化为regression的思路，一次完成目标定位与分类。 基于Faster RCNN中的Anchor，提出了相似的prior box。 加入基于特征金字塔（Pyramidal Feature Hierarchy）的检测方式，即在不同感受野的feature map上预测目标。 这些设计实现了简单的端到端的训练，而且即便使用低分辨率的输入图像也能得到高的精度。 网络结构 采用多尺度特征图用于检测 CNN网络一般前面的特征图比较大，后面会逐渐采用stride=2的卷积或者pool来降低特征图大小，这正如图3所示，一个比较大的特征图和一个比较小的特征图，它们都用来做检测。这样做的好处是比较大的特征图来用来检测相对较小的目标，而小的特征图负责检测大目标。 采用卷积进行检测 SSD直接采用卷积对不同的特征图来进行提取检测结果。对于形状为mnp的特征图，只需要采用33p这样比较小的卷积核得到检测值。（每个添加的特征层使用一系列卷积滤波器可以产生一系列固定的预测）。 设置先验框 SSD借鉴faster rcnn中ancho理念，每个单元设置尺度或者长宽比不同的先验框，预测的是对于该单元格先验框的偏移量，以及每个类被预测反映框中该物体类别的置信度。 模型结构 SSD的模型框架主要由三部分组成，以SSD300为例，有VGG-Base Extra-Layers，Pred-Layers。 VGG-Base作为基础框架用来提取图像的feature，Extra-Layers对VGG的feature做进一步处理，增加模型对图像的感受野，使得extra-layers得到的特征图承载更多抽象信息。待预测的特征图由六种特征图组成，6中特征图最终通过pred-layer得到预测框的坐标，置信度，类别信息。 ","date":"2022-10-26","objectID":"/posts/fpn+ampamp+ssd+ampamp+yolo/:2:0","tags":null,"title":"FPN \u0026 SSD \u0026 YOLO","uri":"/posts/fpn+ampamp+ssd+ampamp+yolo/"},{"categories":null,"content":"YOLO 简介 YOLO在2016年被提出，发表于CVPR。YOLO的全称是you only look once，指只需要浏览一次就可以识别出图中的物体的类别和位置。因为只需要看一次，YOLO被称为Region-free方法，相比于Region-based方法，YOLO不需要提前找到可能存在目标的Region。 具体思路 将一幅图像分成SxS个网格(grid cell)，如果某个object的中心落在这个网格中，则这个网格就负责预测这个object。每个bounding box要预测(x, y, w, h)和confidence共5个值，每个网格还要预测一个类别信息，记为C类。则SxS个网格，每个网格要预测B个bounding box还要预测C个categories。输出就是S x S x (5*B+C)的一个tensor，网络主干是GooleNet。 存在问题 损失函数中localization error和classification error同等重要（解决办法可以是：对没有object的box的confidence loss，赋予小的loss weight；只有当某个网格中有object的时候才对classification error进行更新）。 输出为全连接层，只支持与训练图像相同的输入分辨率。 YOLO对相互靠的很近的物体（挨在一起且中点都落在同一个格子上的情况），还有对很小的物体检测效果不好，这是因为一个网格中只预测了两个框，并且只属于一类。虽然每个格子可以预测B个bounding box，但是最终只选择只选择IOU最高的bounding box作为物体检测输出，即每个格子最多只预测出一个物体。当物体占画面比例较小，如图像中包含畜群或鸟群时，每个格子包含多个物体，但却只能检测出其中一个。这是YOLO方法的一个缺陷。 测试图像中，当同一类物体出现的不常见的长宽比和其他情况时泛化能力偏弱。 对不同大小的box预测中，相比于大box预测偏一点，小box预测偏一点肯定更不能被忍受的。而sum-square error loss中对同样的偏移loss是一样(为了缓和这个问题，作者用了一个比较取巧的办法，就是将box的width和height取平方根代替原本的height和width)。定位误差是影响检测效果的主要原因，尤其是大小物体的处理上，还有待加强。 YOLO loss函数中，大物体IOU误差和小物体IOU误差对网络训练中loss贡献值接近（虽然采用求平方根方式，但没有根本解决问题）。因此，对于小物体，小的IOU误差也会对网络优化过程造成很大的影响，从而降低了物体检测的定位准确性。 ","date":"2022-10-26","objectID":"/posts/fpn+ampamp+ssd+ampamp+yolo/:3:0","tags":null,"title":"FPN \u0026 SSD \u0026 YOLO","uri":"/posts/fpn+ampamp+ssd+ampamp+yolo/"},{"categories":null,"content":"Citation 深度学习中的FPN详解 什么是FPN(Feature Pyramid Networks–特征金字塔)？ SSD网络介绍 YOLO系列知识点整理 Thanks for reading! ","date":"2022-10-26","objectID":"/posts/fpn+ampamp+ssd+ampamp+yolo/:4:0","tags":null,"title":"FPN \u0026 SSD \u0026 YOLO","uri":"/posts/fpn+ampamp+ssd+ampamp+yolo/"},{"categories":null,"content":"ShuffleNet 简介 ShuffleNet是旷视科技提出的一种计算高效的CNN模型，其和MobileNet和SqueezeNet等一样主要是想应用在移动端。所以，ShuffleNet的设计目标也是如何利用有限的计算资源来达到最好的模型精度，这需要很好地在速度和精度之间做平衡。ShuffleNet是一种专门为计算资源有限的设备设计的神经网络结构，主要采用了pointwise group convolution和channel shuffle两种技术，在保留了模型精度的同时极大减少了计算开销。 旧网络存在问题 分组卷积GConv虽然能减少参数量和计算量，但是GConv中不同的组之间的信息没有交流。 在ResNeXt网络中，分组卷积占的计算量很少，大部分计算量都是1*1卷积产生的。 创新点 Channel Shuffle 先通过一个分组卷积，得到对应的特征矩阵，接下来使用channel shuffle操作后的特征矩阵进行组卷积，融合不同组之间的维度信息。 具体为，假设输入的feature map是一个一维的12个数据，分为3组，每一个分组有4个值，channel shuffle操作首先将这个矩阵进行升维，重构为一个g行n列的矩阵（这里g=3，n=4），然后对这个矩阵进行转置，后得到一个n行g列的新矩阵，再按照分组数g对这个得到的新矩阵进行展开，通过这一系列操作，达到了将原来固定的各分组打乱重排的目的。 ShuffleNet Unit ShuffleNet Unit的结构灵感来源于ResNet的中的bottleneck残差模块，从某种意义上来说，二者看起来几乎一样，以下是ShuffleNet Unit的结构： 相较于ResNet的bottleneck模块，ShuffleNet Unit有三处差别： ShuffleNet Unit将bottleneck模块中的3x3卷积替换为了3x3的depthwise卷积，也就是图中的DWConv，如图a所示。 对原残差模块的1x1卷积进行了替换，替换为了1x1的分组卷积，并在后面加上了一个channel shuffle操作，用于消除分组卷积的副作用，如图b所示。 在bottleneck的短路路径上使用了3x3的全局平均池化，然后将原来bottleneck模块的逐元素求和替换为了通道（Add）的拼接操作（Concat），如图c所示。使原来的数值1相加操作变成了维度的相加，更高的维度可以使网络拥有更多的通道数，从而提升网络的性能。 网络结构 基于shufflenet unit模块，整体的ShuffleNet网络结构图如下图所示： 上图为一个图像分类任务，输入为224x224的图像，输出1000个概率，同时作者将分组数分别设置为1到5，以此探索不同的分组数对网络性能的影响，其中一般以g=3作为网络的基准版本。 与传统的神经网络相比，ShuffleNet网络结构最大的区别在于拥有三个shufflenet unit模块，分别对应三个Stage，它们将输入特征图的尺寸减半，通道数加倍，除了升级版的shufflenet unit模块，网络的其他超参数与ResNet保持一致。 ","date":"2022-09-12","objectID":"/posts/shufflenet+ampamp+efficientnet/:1:0","tags":null,"title":"ShuffleNet \u0026 EfficientNet","uri":"/posts/shufflenet+ampamp+efficientnet/"},{"categories":null,"content":"Efficient Net 简介 EfficientNets是google在2019年5月发表的一个网络系列，使用神经架构搜索设计了一个baseline网络，并且将模型进行缩放获得一系列模型。它的精度和效率比之前所有的卷积网络都好。尤其是EfficientNet-B7在ImageNet上获得了当时最先进的 84.4%的top-1精度 和 97.1%的top-5精度，同时比之前最好的卷积网络大小缩小了8.4倍、速度提高了6.1倍。EfficientNets也可以很好的迁移，并且以更少的参量实现了最先进的精度——CIFAR-100（91.7%）、Flowers（98.8%）。 背景 通常为了获得更好的精度，放大卷积神经网络是一种广泛的方法。举个例子，ResNet可以通过使用更多层从ResNet-18放大到ResNet-200；目前为止，GPipe通过将baseline模型放大四倍在ImageNet数据集上获得了84.3%的top-1精度，然而，放大CNN的过程从来没有很好的理解过，目前通用的几种方法是放大CNN的深度、宽度和分辨率，在之前都是单独放大这三个维度中的一个，尽管任意放大两个或者三个维度也是可能的，但是任意缩放需要繁琐的人工调参同时可能产生的是一个次优的精度和效率。 模型缩放方法 Single Scaling（单尺度） 深度（d）：缩放网络深度在许多ConvNets都有使用，直觉上更深的网络可以捕获到更丰富和更复杂的特征，在新任务上也可以泛化的更好。然而，更深的网络由于梯度消失问题（这里我更倾向于说成是网络退化问题）也更难训练。尽管有一些技术，例如跨层连接、批量归一化等可以有效减缓训练问题，但是深层网络的精度回报减弱了：举个例子，ResNet-1000和ResNet-101具有类似的精度，即使它的层数更多。 宽度（w）：缩放网络宽度也是一种常用的手段，正如之前讨论过的，更宽的网络可以捕捉到更细粒度的特征从而易于训练。然而，非常宽而又很浅的网络在捕捉高层次特征时有困难。 分辨率（r）：使用更高分辨率的输入图像，ConvNets可能可以捕捉到更细粒度的模式。从最早的 224x224，现在有些ConvNets为了获得更高的精度选择使用 229x229 或者 331x331。目前，GPipe使用 480x480 的分辨率获得了最先进的ImageNet精度，更好的精度比如 600x600 也被广泛使用在目标检测网络中。 Compound Scaling（混合尺度） 论文中提出了Compound Scaling，同时对通过NAS得到的baseline模型Efficient-b0的深度（depth）、宽度（width）、输入图片分辨率（resolution）进行scaling，和 AutoML技术得到一系列网络模型EfficientNets。 为了了解网络缩放的效果，作者系统地研究了缩放不同维数对模型的影响。 虽然缩放单个维度可以提高模型性能，但作者观察到，根据可用资源平衡网络的所有维度ーー宽度、深度和图像分辨率ーー可以最大限度地提高整体性能。 复合缩放方法的第一步是执行网格搜索，以找到在固定资源约束下基线网络的不同缩放维度之间的关系。这决定了上面提到的每个维度的适当比例系数。 然后应用这些系数扩大基线网络，以达到期望的模型大小或资源要求。 与传统的缩放方法（上图a-d）相比，这种复合缩放方法（上图e）不断提高模型的精度和效率，可用于扩展现有的模型，如 mobileet (+ 1.4% 的图像集精度)和 ResNet (+ 0.7%)。 网络结构 作者指明，由于模型缩放不会改变基线网络中的层，但是拥有一个良好的基线网络也是至关重要的。作者使用现有的基础网络来评估该缩放方法，但是为了更好地证明该缩放方法的有效性，作者还开发了一种新的mobile-size baseline，称为 EfficientNet，EfficientNet-B0的网络结构如下 (类似于 MobileNetV2 和 MnasNet)： ","date":"2022-09-12","objectID":"/posts/shufflenet+ampamp+efficientnet/:2:0","tags":null,"title":"ShuffleNet \u0026 EfficientNet","uri":"/posts/shufflenet+ampamp+efficientnet/"},{"categories":null,"content":"Citation 【轻量化网络】ShuffleNet V1 论文研读 论文阅读笔记：ShuffleNet 令人拍案叫绝的EfficientNet和EfficientDet EfficientNet详解 Thanks for reading! ","date":"2022-09-12","objectID":"/posts/shufflenet+ampamp+efficientnet/:3:0","tags":null,"title":"ShuffleNet \u0026 EfficientNet","uri":"/posts/shufflenet+ampamp+efficientnet/"},{"categories":null,"content":"SENet 简介 SENet是2017ILSVRC2017（ImageNet Large Scale Visual Recognition Challenge）竞赛的冠军网络，在CVPR2018引用量第一。在深度学习领域，已经有很多成果通过在空间维度上对网络的性能进行了提升。而SENet反其道而行之，通过对通道关系进行建模来提升网络的性能。SENet较早的将注意力机制引入CNN中，使用了模块化设计。 亮点 引入通道注意力机制，关注channel之间的关系，希望模型可以自动学习到不同channel特征的重要程度。 网络结构 原理 SENet网络的创新点在于关注channel之间的关系，希望模型可以自动学习到不同channel特征的重要程度。为此，SENet提出了Squeeze-and-Excitation (SE)模块，SE模块首先对卷积得到的特征图进行Squeeze操作，得到channel级的全局特征，然后对全局特征进行Excitation操作，学习各个channel间的关系，也得到不同channel的权重，最后乘以原来的特征图得到最终特征。 本质上，SE模块是在channel维度上做attention或者gating操作，这种注意力机制让模型可以更加关注信息量最大的channel特征，而抑制那些不重要的channel特征。另外一点是SE模块是通用的，这意味着其可以嵌入到现有的网络架构中。 Squeeze 原始feature map的维度为H×W×C，其中H是高度（Height），W是宽度（width），C是通道数（channel）。Squeeze就是用全局平均池化（global average pooling）把H×W×C压缩为1×1×C。H×W压缩成一维后，相当于这一维参数获得了之前H×W全局的视野，感受区域更广。公式如下： Excitation Sequeeze操作得到了全局描述特征，接下来需要另外一种运算来提取channel之间的关系。 用两个全连接层来学习通道间的相关性，第一个FC层起到降维的作用，然后采用ReLU激活。最后的全连接层恢复原始的维度。公式如下： Reweight 将Excitation的输出权重作为经过特征选择后的每个特征通道的重要性，然后通过乘法逐通道加权到先前的特征上，完成在通道维度上的对原始特征的重标定。 全局平均池化 思想：对于输出的每一个通道的特征图的所有像素计算一个平均值，经过全局平均池化之后就得到一个 维度=类别数的特征向量，然后直接输入到softmax层。 作用：代替全连接层，可接受任意尺寸的图像。 优点： 可以更好的将类别与最后一个卷积层的特征图对应起来（每一个通道对应一种类别，这样每一张特征图都可以看成是该类别对应的类别置信图）； 降低参数量，全局平均池化层没有参数，可防止在该层过拟合； 整合了全局空间信息，对于输入图片的spatial translation更加鲁棒。 结构融合 上图是将SE模块嵌入到Inception结构的示例。方框旁边的维度代表该层的输出。这里使用Alobal Average Pooling作为Squeeze操作。紧接着两个Fully Connected层组成一个Bottleneck结构去建模通道间的相关性，并输出和输入特征同样数目的权重。首先将特征维度降低到输入的1/16，然后经过ReLu激活后再通过一个Fully Connected层升回到原来的维度。这样做相比直接用一个Fully Connected层的好处在于： 具有更多的非线性，可以更好地拟合通道间复杂的相关性； 减少了参数量和计算量。 下图是SE模块嵌入到ResNet的示例。操作过程基本和SE-Inception一样，只不过是在Addition前对分支上Residual的特征进行了特征重标定。如果对Addition后主支上的特征进行重标定，由于在主干上存在0~1的scale操作，在网络较深BP优化时就会在靠近输入层容易出现梯度消散的情况，导致模型难以优化。 运算效率 实验表明，SE模块在参数量上的增加带来的计算量增长微乎其微，但是性能却有所提升，当然这也取决于实际应用，如果因为SE模块导致参数量增加的掠夺，可以针对性的在适当的位置削减SE模块的数量，而精度几乎不受影响。 以ResNet-50和SE-ResNet-50对比举例来说，SE-ResNet-50相对于ResNet-50有着10%模型参数的增长。额外的模型参数都存在于Bottleneck设计的两个Fully Connected中，由于ResNet结构中最后一个stage的特征通道数目为2048，导致模型参数有着较大的增长，实验发现移除掉最后一个stage中3个build block上的SE设定，可以将10%参数量的增长减少到2%。此时模型的精度几乎无损失。 ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:1:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"MobileNet 简介 MobileNet V1是由google2016年提出，2017年发布的文章。其主要创新点在于深度可分离卷积，而整个网络实际上也是深度可分离模块的堆叠。它基于流线型架构，使用深度可分离卷积来构建轻量级深度神经网络，用于移动和嵌入式视觉应用。该网络引入了两个简单的全局超参数——宽度乘数和分辨率乘数，可以有效地在延迟和准确性之间进行权衡。 亮点 深度可分离卷积的使用 Global Average Pooling 的使用 用CONV/s2（步进2的卷积）代替MaxPool+CONV 深度可分离卷积的使用 在进行 depthwise 卷积时只使用了一种维度为in_channels的卷积核进行特征提取（没有进行特征组合）。采用 depth-wise convolution 会有一个问题，就是导致 信息流通不畅 ，即输出的 feature map 仅包含输入的 feature map 的一部分，在这里，MobileNet 采用了 point-wise(1*1) convolution 帮助信息在通道之间流通。 在进行 pointwise 卷积时只使用了output_channels 种 维度为in_channels 1*1 的卷积核进行特征组合，普通卷积不同 depth 层的权重是按照 1:1:1…:1的比例进行相加的，而在这里不同 depth 层的权重是按照不同比例(可学习的参数) 进行相加的。 参数数量由原来的p1 = F*F*in_channels*output_channels 变为了p2 = F*F*in_channels*1 + 1*1*in_channels*output_channels，减小为原来的p2/p1 = 1/output_channels + 1/F*F，其中 F 为卷积核的尺寸，若 F = 3，参数量大约会减少到原来的 1/8-1/9 。 原论文中对第一层没有用此卷积，深度可分离卷积中的每一个后面都跟 BN 和 RELU。其中，Relu6=min(max(0,x),6)。 ReLU6就是普通的ReLU但是限制最大输出值为6（对输出值做clip），这是为了在移动端设备float16的低精度的时候，也能有很好的数值分辨率，如果对ReLU的激活范围不加限制，输出范围为0到正无穷，如果激活值非常大，分布在一个很大的范围内，则低精度的float16无法很好地精确描述如此大范围的数值，带来精度损失。 两个超参数 Width Multiplier(α): Thinner Models 所有层的通道数（channel） 乘以 α 参数，模型大小近似下降到原来的 α^2 倍，计算量下降到原来的 α^2倍，α ∈ ( 0 , 1 ] with typical settings of 1, 0.75, 0.5 and 0.25，降低模型的宽度。 Resolution Multiplier(ρ \\rhoρ): Reduced Representation 输入层的 分辨率（resolution） 乘以 ρ 参数，等价于所有层的分辨率乘 ρ ，模型大小不变，计算量下降到原来的 ρ^2 倍，ρ ∈ ( 0 , 1 ]，降低输入图像的分辨率。 ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:2:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"MobileNet V2 简介 MobileNet V2基于倒置残差结构(inverted residual structure)，原本的残差结构的主分支是有三个卷积，两个逐点卷积通道数较多，而倒置的残差结构刚好相反，中间的卷积通道数（依旧使用深度分离卷积结构）较多，旁边的较小。此外，发现去除主分支中的非线性变换是有效的，这可以保持模型表现力。论文在多个数据集上做了对比实验，验证了该架构的有效性。 MobileNet V1缺点 结构问题 V1结构过于简单，没有复用图像特征，即没有concat/eltwise+ 等操作进行特征融合，而后续的一系列的ResNet，DenseNet等结构已经证明复用图像特征的有效性。 逐深度卷积问题： 在处理低维数据（比如逐深度的卷积）时，relu函数会造成信息的丢失。 DW 卷积由于本身的计算特性决定它自己没有改变通道数的能力，上一层给它多少通道，它就只能输出多少通道。所以如果上一层给的通道数本身很少的话，DW 也只能很委屈的在低维空间提特征，因此效果不够好。 主要改进点 引入倒残差（Inverted Residuals）结构，先升维再降维，增强梯度的传播，显著减少推理期间所需的内存占用。 去掉 Narrow layer（low dimension or depth） 后的 ReLU，保留特征多样性，增强网络的表达能力（Linear Bottlenecks）。 网络为全卷积的，使得模型可以适应不同尺寸的图像；使用 RELU6（最高输出为 6）激活函数，使得模型在低精度计算下具有更强的鲁棒性。 MobileNet V2 building block 如下所示，若需要下采样，可在 DWise 时采用步长为 2 的卷积；小网络使用小的扩张系数（expansion factor），大网络使用大一点的扩张系数（expansion factor），推荐是5~10，论文中 t = 6t。 具体改进 V2 去掉了第二个 PW 的激活函数改为线性激活。 论文作者称其为 Linear Bottleneck。原因如上所述是因为作者认为激活函数在高维空间能够有效的增加非线性，而在低维空间时则会破坏特征，不如线性的效果好。 V2 在 DW 卷积之前新加了一个 PW 卷积。 给每个 DW 之前都配备了一个 PW，专门用来升维，定义升维系数 t=6 ，这样不管输入通道数 Cin 是多是少，经过第一个 PW 升维之后，DW 都是在相对的更高维 ( t.Cin ) 进行更好的特征提取。 ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:3:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"MobileNet V3 简介 MobileNet V3是Google继MobileNetV2之后的又一力作，作为MobileNet系列的新成员，自然效果会提升，本文的一个亮点在于，网络的设计利用了NAS（network architecture search）算法以及NetAdapt algorithm算法。并且，本文还介绍了一些提升网络效果的trick，这些trick也提升了不少的精度以及速度。 亮点 互补搜索技术组合：由资源受限的NAS执行模块级搜索，NetAdapt执行局部搜索。 网络结构改进：将最后一步的平均池化层前移并移除最后一个卷积层，引入h-swish激活函数。 具体改进措施 引入SE结构 在bottlenet结构中加入了SE结构，并且放在了depthwise filter之后，如下图。因为SE结构会消耗一定的时间，所以作者在含有SE的结构中，将expansion layer的channel变为原来的1/4,这样作者发现，即提高了精度，同时还没有增加时间消耗。并且SE结构放在了depthwise之后。 修改尾部结构 在MobileNet V2中，avg pooling之前，存在一个1x1的卷积层，目的是提高特征图的维度，更有利于结构的预测，但是这其实带来了一定的计算量了，所以这里作者修改了，将其放在avg pooling的后面，首先利用avg pooling将特征图大小由7x7降到了1x1，降到1x1后，然后再利用1x1提高维度，这样就减少了7x7=49倍的计算量。并且为了进一步的降低计算量，作者直接去掉了前面纺锤型卷积的3x3以及1x1卷积，进一步减少了计算量，就变成了如下图第二行所示的结构，作者将其中的3x3以及1x1去掉后，精度并没有得到损失。这里降低了大约15ms的速度。 修改channel数量 修改头部卷积核channel数量，MobileNet V2中使用的是32 x 3 x 3，作者发现，其实32可以再降低一点，所以这里作者改成了16，在保证了精度的前提下，降低了3ms的速度。 非线性变换的改变 使用h-swish替换swish，swish是谷歌自家的研究成果，这次在其基础上，为速度进行了优化。swish与h-swish公式如下所示，由于sigmoid的计算耗时较长，特别是在移动端，这些耗时就会比较明显，所以作者使用ReLU6(x+3)/6来近似替代sigmoid，观察下图可以发现，其实相差不大的。利用ReLU有几点好处：1.可以在任何软硬件平台进行计算，2.量化的时候，它消除了潜在的精度损失，使用h-swish替换swith，在量化模式下回提高大约15%的效率，另外，h-swish在深层网络中更加明显。使用h-swish@16可以提高大约0.2%的精度，但是时间延长了大约20%。 设计方法 先使用NAS算法，优化每一个block，得到大体的网络结构，然后使用NetAdapt 算法来确定每个filter的channel的数量。 这里由于small model的精度以及耗时影响相对较大，mobilenet v3 large和mobilenet v3 small是分别使用NAS设计的。 NAS之后，可以使用NetAdapt算法设计每个layer，过程如下： 先用NAS找到一个可用的结构A。 在A的基础上生成一系类的候选结构，并且这些候选结构消耗在一点点减少，其实就是穷举子结构。 对于每个候选结构，使用前一个模型进行初始化，（前一个模型没有的参数随机初始化就行），finetune T个epoch，得到一个大致的精度。 在这些候选结构中，找到最好的。 反复迭代，知道目标时间到达，找到最合适的结果。 ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:4:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"Summary MobileNetV1 = VGG的标准卷积换成深度可分离卷积 MobileNetV2 = V1 + Inverted residuals + shortcut MobileNetV3 = V2 + SE-NET + h-swish ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:5:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"Citation SENet源码 SENet概览 论文解读《SENet》 一文带你深入全局平均池化 轻量级网络-Mobilenet系列(v1,v2,v3) MobileNetV1 \u0026 MobileNetV2 简介 轻量化网络——MobileNet mobilenet系列之又一新成员—mobilenet-v3 Thanks for reading! ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:6:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"ResNet 简介 ResNet网络在 2015年由微软实验室中的何凯明等人提出，斩获当年ImageNet竞赛中分类任务第一名，目标检测第一名。获得COCO数据集中目标检测第一名，图像分割第一名。 亮点 超深的网络结构（超过1000层） 提出residual（残差结构）模块 使用Batch Normalization加速训练（丢弃Dropout） 网络结构 为什么采用residual 在ResNet提出之前，所有的神经网络都是通过卷积层和池化层的叠加组成的。人们认为卷积层和池化层的层数越多，获取到的图片特征信息越全，学习效果也就越好。但是在实际的试验中发现，随着卷积层和池化层的叠加，不但没有出现学习效果越来越好的情况，反而出现两种问题： 梯度消失和梯度爆炸。目前优化神经网络的方法都是基于BP，即根据损失函数计算的误差通过梯度反向传播的方式，指导深度网络权值的更新优化。其中将误差从末层往前传递的过程需要链式法则（Chain Rule）的帮助。而链式法则是一个连乘的形式，所以当层数越深的时候，梯度将以指数形式传播。梯度消失问题和梯度爆炸问题一般随着网络层数的增加会变得越来越明显。在根据损失函数计算的误差通过梯度反向传播的方式对深度网络权值进行更新时，得到的梯度值接近0或特别大，也就是梯度消失或爆炸。 退化问题。随着层数的增加，反传回来的梯度之间的相关性会越来越差，最后接近白噪声，预测效果反而越来越差。如下图所示 为了解决梯度消失或梯度爆炸问题，ResNet论文提出通过数据的预处理以及在网络中使用BN（Batch Normalization）层来解决。 为了解决深层网络中的退化问题，可以人为地让神经网络某些层跳过下一层神经元的连接，隔层相连，弱化每层之间的强联系。这种神经网络被称为残差网络 (ResNets)。ResNet论文提出了residual结构（残差结构）来减轻退化问题，下图是使用residual结构的卷积网络，可以看到随着网络的不断加深，效果并没有变差，而是变的更好了。 residual结构 residual结构使用了一种shortcut的连接方式，也可理解为捷径。让特征矩阵隔层相加，注意F(X)和X形状要相同，所谓相加是特征矩阵相同位置上的数字进行相加。若某一较深的网络多出另一较浅网络的若干层有能力学习到恒等映射，那么这一较深网络训练得到的模型性能一定不会弱于该浅层网络。 两种不同的residual： 对于深层次网络，使用左边的block意味着有很大的计算量，因此右侧使用1x1卷积先将输入进行降维，然后再经过3x3卷积后，最后用1x1卷积进行升维，为了主分支上输出的特征矩阵和捷径分支上输出的特征矩阵形状相同，以便进行加法操作。（搭建深层次网络时，采用右侧的残差结构） 降维时的shortcut 观察ResNet18层网络，可以发现有些残差块的shortcut是实线的，而有些则是虚线的。这些虚线的shortcut上通过1×1的卷积核进行了维度处理（特征矩阵在长宽方下采样，深度方向调整成下一层残差结构所需要的channel）。 Batch Normalization Batch Normalization是指批标准化处理，将一批数据的feature map满足均值为0，方差为1的分布规律。 对图像进行标准化处理，这样能够加速网络的收敛，如下图所示，对于Conv1来说输入的就是满足某一分布的特征矩阵，但对于Conv2而言输入的feature map就不一定满足某一分布规律了（注意这里所说满足某一分布规律并不是指某一个feature map的数据要满足分布规律，理论上是指整个训练样本集所对应feature map的数据要满足分布规律）。而我们Batch Normalization的目的就是使我们的feature map满足均值为0，方差为1的分布规律。计算公式如下： 假设feature1、feature2分别是由image1、image2经过一系列卷积池化后得到的特征矩阵，feature的channel为2，那么代表该batch的所有feature的channel1的数据，同理代表该batch的所有feature的channel2的数据。然后分别计算和的均值与方差，得到两个向量。然后在根据标准差计算公式分别计算每个channel的值（公式中的是一个很小的常量，防止分母为零的情况）。在我们训练网络的过程中，我们是通过一个batch一个batch的数据进行训练的，但是我们在预测过程中通常都是输入一张图片进行预测，此时batch size为1，如果在通过上述方法计算均值和方差就没有意义了。所以我们在训练过程中要去不断的计算每个batch的均值和方差，并使用移动平均(moving average)的方法记录统计的均值和方差，在训练完后我们可以近似认为所统计的均值和方差就等于整个训练集的均值和方差。然后在我们验证以及预测过程中，就使用统计得到的均值和方差进行标准化处理。如下图： ","date":"2022-08-26","objectID":"/posts/resnet+ampamp+densenet/:1:0","tags":null,"title":"ResNet \u0026 DenseNet","uri":"/posts/resnet+ampamp+densenet/"},{"categories":null,"content":"ResNet V2 简介 ResNet V2 的主要贡献在于通过理论分析和大量实验证明使用恒等映射（identity mapping）作为快捷连接（skip connection）对于残差块的重要性。同时，将BN/ReLu这些activation操作挪到了Conv（真正的weights filter操作）之前，提出预激活（Pre-activation）操作，并通过与后激活（Post-activation）操作做对比实验，表明对于多层网络，使用了预激活残差单元（Pre-activation residual unit）的resnet v2都取得了比 原版本resnet更好的结果。 亮点 提出了新的残差模块结构： 将激活函数移至旁路 full pre-activation 深度残差网络的分析 原先resnets的残差单元可表示为： 如果函数f也是恒等映射，则公式可以合并为： 那么任意深层的单元L与浅层单元l之间的关系可表示为： 该公式有以下两个特性： 深层单元的特征可以由浅层单元的特征和残差函数相加得到； 任意深层单元的特征都可以由起始特征x0与先前所有残差函数相加得到，这与普通（plain）网络不同，普通网络的深层特征是由一系列的矩阵向量相乘得到。残差网络是连加，普通网络是连乘。 该公式也带来了良好的反向传播特性，用ε表示损失函数，根据反向传播的链式传导规则，反向传播公式如下： 从上述公式中可以看出，反向传播也是两条路径，其中之一直接将信息回传，另一条会经过所有的带权重层。另外可以注意到第二项的值在一个mini-batch中不可能一直是-1，也就是说回传的梯度不会消失，不论网络中的权值的值再小都不会发生梯度消失现象。 恒等映射的重要性 考虑恒等映射的重要性。假设将恒等映射改为 $$ h(x_l) = \\lambda_l x_l $$ 则有： 递归调用得： 其中F表示将标量合并到残差函数中，反向传播公式如下： 与之前的公式不同，上公式的第一个加法项由因子 ∏i=lL−1λi 进行调节。对于一个极深的网络( L极大)，考虑第一个连乘的项，如果所有的 λ 都大于1，那么这一项会指数级增大；如果所有 λ 都小于1，那么这一项会很小甚至消失，会阻断来自shortcut的反向传播信号，并迫使其流过权重层。本文通过实验证明这种方式会对模型优化造成困难。另外其他不同形式的变换映射也都会阻碍信号的有效传播，进而影响训练进程。 不同形式激活函数的效果 BN after addition 效果比基准差，BN 层移到相加操作后面会阻碍信号传播，一个明显的现象就是训练初期误差下降缓慢。 ReLU before addition 这样组合的话残差函数分支的输出就一直保持非负，这会影响到模型的表示能力，而实验结果也表明这种组合比基准差。 Post-activation or pre-activation 原来的设计中相加操作后面还有一个ReLU激活函数，这个激活函数会影响到残差单元的两个分支，现在将它移到残差函数分支上，快捷连接分支不再受到影响。具体操作如下图： 根据激活函数与相加操作的位置关系，我们称之前的组合方式为“后激活（post-activation）”，现在新的组合方式称之为“预激活（pre-activation）”。预激活方式又可以分为两种：只将ReLU放在前面，或者将ReLU和BN都放到前面，实验结果显示full pre-activation的效果要更好。 使用预激活有两个方面的优点： f变为恒等映射，使得网络更易于优化； 使用BN作为预激活可以加强对模型的正则化。 Ease of optimization 这在训练1001层残差网络时尤为明显。使用原来设计的网络在起始阶段误差下降很慢，因为f是ReLU激活函数，当信号为负时会被截断，使模型无法很好地逼近期望函数；而使用预激活网络中的f是恒等映射，信号可以在不同单元直接直接传播。本文使用的1001层网络优化速度很快，并且得到了最低的误差。 f为ReLU对浅层残差网络的影响并不大。本文认为是当网络经过一段时间的训练之后权值经过适当的调整，使得单元输出基本都是非负，此时f不再对信号进行截断。但是截断现象在超过1000层的网络中经常发生。 Reducing overfitting 使用了预激活的网络的训练误差稍高，但却得到更低的测试误差，本文推测这是BN层的正则化效果所致。在原始残差单元中，尽管BN对信号进行了标准化，但是它很快就被合并到捷径连接(shortcut)上，组合的信号并不是被标准化的。这个非标准化的信号又被用作下一个权重层的输入。与之相反，本文的预激活（pre-activation）版本的模型中，权重层的输入总是标准化的。 ","date":"2022-08-26","objectID":"/posts/resnet+ampamp+densenet/:2:0","tags":null,"title":"ResNet \u0026 DenseNet","uri":"/posts/resnet+ampamp+densenet/"},{"categories":null,"content":"ResNeXt 简介 ResNeXt是ResNet和Inception的结合体，不同于Inception v4的是，ResNext不需要人工设计复杂的Inception结构细节，而是每一个分支都采用相同的拓扑结构。ResNeXt的本质是分组卷积（Group Convolution），通过变量基数（Cardinality）来控制组的数量。分组卷机是普通卷积和深度可分离卷积的一个折中方案，即每个分支产生的Feature Map的通道数为 n(n\u003e1) 。 神经网络标准范式 split-transform-merge 如上图所示，先将输入分配到多路，然后每一路进行转换，最后再把所有支路的结果融合。 基本结构 具体结构 类似ResNet，作者选择了很简单的基本结构，每一组C个不同的分支都进行相同的简单变换，上面是ResNeXt-50（32x4d）的配置清单，32指进入网络的第一个ResNeXt基本结构的分组数量C（即基数）为32，4d表示depth即每一个分组的通道数为4（所以第一个基本结构输入通道数为128）。 可以看到ResNet-50和ResNeXt-50（32x4d）拥有相同的参数，但是精度却更高。具体实现上，因为1x1卷积可以合并，就合并了，代码更简单，并且效率更高。 得益于精心设计的复杂的网络结构，ResNet-Inception v2可能效果会更好一点，但是ResNeXt的网络结构更简单，可以防止对于特定数据集的过拟合。而且更简单的网络意味着在用于自己的任务的时候，自定义和修改起来更简单。 总结 ResNeXt提出了一种介于普通卷积核深度可分离卷积的这种策略：分组卷积，他通过控制分组的数量（基数）来达到两种策略的平衡。分组卷积的思想是源自Inception，不同于Inception的需要人工设计每个分支，ResNeXt的每个分支的拓扑结构是相同的。最后再结合残差网络，得到的便是最终的ResNeXt。 从上面的分析中我们可以看书ResNeXt的结构非常简单，但是其在ImageNet上取得了由于相同框架的残差网络，也算是Inception直接助攻了一把吧。 ResNeXt确实比Inception V4的超参数更少，但是他直接废除了Inception的囊括不同感受野的特性仿佛不是很合理，在更多的环境中我们发现Inception V4的效果是优于ResNeXt的。类似结构的ResNeXt的运行速度应该是优于Inception V4的，因为ResNeXt的相同拓扑结构的分支的设计是更符合GPU的硬件设计原则。 ","date":"2022-08-26","objectID":"/posts/resnet+ampamp+densenet/:3:0","tags":null,"title":"ResNet \u0026 DenseNet","uri":"/posts/resnet+ampamp+densenet/"},{"categories":null,"content":"DenseNet 简介 DenseNet模型的基本思路与ResNet一致，但是它建立的是前面所有层与后面层的密集连接。DenseNet脱离了加深网络层数(ResNet)和加宽网络结构(Inception)来提升网络性能的定式思维，从特征的角度考虑，通过特征重用和旁路(Bypass)设置，既大幅度减少了网络的参数量，又在一定程度上缓解了gradient vanishing问题的产生。这些特点让DenseNet在参数和计算成本更少的情形下实现比ResNet更优的性能，DenseNet也因此斩获CVPR 2017的最佳论文奖。 亮点 由于密集连接方式，DenseNet提升了梯度的反向传播，使得网络更容易训练。由于每层可以直达最后的误差信号，实现了隐式的“deep supervision”。 参数更小且计算更高效，这有点违反直觉，由于DenseNet是通过concat特征来实现短路连接，实现了特征重用，并且采用较小的growth rate，每个层所独有的特征图是比较小的。 由于特征复用，最后的分类器使用了低级特征。 设计理念 相比ResNet，DenseNet提出了一个更激进的密集连接机制：即互相连接所有的层，具体来说就是每个层都会接受其前面所有层作为其额外的输入。下图1为ResNet网络的连接机制，作为对比，下图2为DenseNet的密集连接机制。可以看到，ResNet是每个层与前面的某层（一般是2~3层）短路连接在一起，连接方式是通过元素级相加。而在DenseNet中，每个层都会与前面所有层在channel维度上连接（concat）在一起（这里各个层的特征图大小是相同的），并作为下一层的输入。对于一个L层的网络，DenseNet共包含L(L+1)/2个连接，相比ResNet，这是一种密集连接。而DenseNet是直接concat来自不同层的特征图，这可以实现特征重用，提升效率，这一特点是DenseNet与ResNet最主要的区别。 网络结构 假设输入为一个图片 X0 , 经过一个L层的神经网络，其中第i层的非线性变换记为 Hi ()，Hi ()可以是多种函数操作的累加如BN、ReLU、Pooling或Conv等。第i层的特征输出记作 Xi。 传统卷积前馈神经网络将第i层的输出Xi作为i+1层的输入，可以写作Xi = Hi ( Xi−1 )。ResNet增加了旁路连接，可以写作Xl = Xl ( Xl−1 )+ Xl−1。 ResNet的一个最主要的优势便是梯度可以流经恒等函数来到达靠前的层，但恒等映射和非线性变换输出的叠加方式是相加，这在一定程度上破坏了网络中的信息流。 为了进一步优化信息流的传播，DenseNet提出了上图的网络结构。如图所示，第i层的输入不仅与i-1层的输出相关，还有所有之前层的输出有关，记作: Xl = Hl ([ X0 , X1 ,…, Xl−1 ])， 其中[]代表concatenation(拼接)，既将 X0 到 Xl−1 层的所有输出feature map按Channel组合在一起。这里所用到的非线性变换H为BN+ReLU+ Conv(3×3)的组合。 DenseBlock+Transition CNN网络一般要经过Pooling或者stride\u003e1的Conv来降低特征图的大小，而DenseNet的密集连接方式需要特征图大小保持一致。为了解决这个问题，DenseNet网络中使用DenseBlock+Transition的结构。 DenseBlock是包含很多层的模块，每个层的特征图大小相同，层与层之间采用密集连接方式。 Transition模块是连接两个相邻的DenseBlock，并且通过Pooling使特征图大小降低。 总结 一些较早层提取出的特征仍可能被较深层直接使用。 即使是Transition layer也会使用到之前Denseblock中所有层的特征。 第2-3个Denseblock中的层对之前Transition layer利用率很低,说明transition layer输出大量冗余特征.这也为DenseNet-BC提供了证据支持,既Compression的必要性。 最后的分类层虽然使用了之前Denseblock中的多层信息,但更偏向于使用最后几个feature map的特征,说明在网络的最后几层,某些high-level的特征可能被产生。 ","date":"2022-08-26","objectID":"/posts/resnet+ampamp+densenet/:4:0","tags":null,"title":"ResNet \u0026 DenseNet","uri":"/posts/resnet+ampamp+densenet/"},{"categories":null,"content":"Citation ResNet详解 你必须要知道CNN模型：ResNet Backbone论文-ResNet v2 解读 深度学习——分类之ResNeXt ResNeXt详解 详解DenseNet(密集连接的卷积网络) DenseNet详解 Thanks for reading! ","date":"2022-08-26","objectID":"/posts/resnet+ampamp+densenet/:5:0","tags":null,"title":"ResNet \u0026 DenseNet","uri":"/posts/resnet+ampamp+densenet/"},{"categories":null,"content":"AlexNet 简介 AlexNet是2012年ISLVRC 2012（ImageNet Large Scale Visual Recognition Challenge）竞赛的冠军网络，分类准确率由传统的 70%+提升到 80%+。由Hinton和他的学生Alex Krizhevsky设计。也是在那年之后，深度学习开始迅速发展。 亮点 首次使用GPU进行模型训练。 使用了ReLU激活函数，而不是传统的Sigmoid激活函数以及Tanh激活函数。主要原因是ReLU函数在进行梯度下降的计算过程中能显著加快训练过程，也就是非饱和的线性的激活函数要快于sigmoid和tanh等饱和的非线性激活函数的收敛速度（在这篇论文中并没有谈及ReLU函数对于梯度消失问题的解决，只是从收敛速度上论述的）。 使用了LRN局部响应归一化（Local Response Normalization，LRN）。AlexNet中提出局部响应归一化层（LRN）,是跟在激活或池化之后的一种为了提高准确度的技术方法。LRN的主要思想就是对局部神经元的神经活动创建竞争机制，使得其中响应比较大的值变得相对更大，并抑制其他反馈更小的神经元，从而也在一定程度上增强了泛化能力。 在全连接层的前两层中使用了Dropout随机失活神经元操作，以减少过拟合。 模型架构 ReLU激活函数 针对sigmoid梯度饱和导致训练收敛慢的问题，在AlexNet中引入了ReLU。ReLU是一个分段线性函数，小于等于0则输出为0；大于0的则恒等输出。相比于sigmoid，ReLU有以下有特点： 计算开销小。sigmoid的正向传播有指数运算，倒数运算，而ReLu是线性输出；反向传播中，sigmoid有指数运算，而ReLU有输出的部分，导数始终为1。 梯度饱和问题。 稀疏性。ReLU会使一部跟神经元的输出为0，这样造就了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 Dropout 过拟合的根本原因是特征维度过多，模型过于复杂，参数过多，训练数据过少，噪声过多，导致模型完美拟合训练集，但缺乏泛化能力，对新数据的测试集预测结果差。 Dropout的使用有效地缓解了模型复杂度提升导致的过拟合。Dropout通过修改神经网络本身结构来实现，对于某一层的神经元，通过定义的概率将神经元置为0，这个神经元就不参与前向和后向传播，就如同在网络中被删除了一样，同时保持输入层与输出层神经元的个数不变，然后按原有的学习方法进行参数更新。在下一次迭代中，又重新随机删除一些神经元（置为0），直至训练结束。 Dropout应该算是AlexNet中一个很大的创新，现在神经网络中的必备结构之一。Dropout也可以看成是一种模型组合，每次生成的网络结构都不一样，通过组合多个模型的方式能够有效地减少过拟合，Dropout只需要两倍的训练时间即可实现模型组合（类似取平均）的效果，非常高效。 ","date":"2022-08-20","objectID":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/:1:0","tags":null,"title":"AlexNet \u0026 VGG \u0026 GoogLeNet","uri":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/"},{"categories":null,"content":"VGG 简介 VGG是由牛津大学视觉几何小组（Visual Geometry Group, VGG）提出的一种深层卷积网络结构，赢得了 2014 年 ILSVRC 分类任务的亚军（冠军由 GoogLeNet 夺得）和定位任务的冠军。VGG可以看成是加深版本的AlexNet，都是conv layer + FC layer。该网络是在ILSVRC 2014上的相关工作，主要工作是证明了增加网络的深度能够在一定程度上影响网络最终的性能。 亮点 证明了增加网络的深度能够在一定程度上影响网络最终的性能。 小卷积核和小池化核，卷积核全部替换为3*3（极少用了1*1），相比AlexNet的3*3池化核，VGG全部为2*2的池化核。 层数更深特征图更宽，由于卷积核专注于扩大通道数、池化专注于缩小宽和高，使得模型架构上更深更宽的同时，计算量的增加放缓。 全连接层换成卷积层，网络测试阶段将训练阶段的三个全连接替换为三个卷积，测试重用训练时的参数，使得测试得到的全卷积网络因为没有全连接的限制，因而可以接收任意宽或高为的输入。 模型架构 网络特点 两个3x3的卷积堆叠获得的感受野大小，相当一个5x5的卷积；而3个3x3卷积的堆叠获取到的感受野相当于一个7x7的卷积。好处如下： 多层卷积引入了多次非线性变换，可以增加网络的非线性表达能力。 两层3*3卷积核的参数量比一层5*5卷积核的参数量更少，可以看作对网络做了相应的正则化。 网络测试阶段将训练阶段的三个全连接替换为三个卷积，使得测试阶段对输入图片尺寸没有限制。使用全连接层和卷积层的区别： ​ 全连接层：通道数相同的情况下，特征图大小一样，才能保证与全连接层中的神经元数目相同。 ​ 卷积层：通道数目来决定最终的分类输出数目，不需要特征图大小相同。 ","date":"2022-08-20","objectID":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/:2:0","tags":null,"title":"AlexNet \u0026 VGG \u0026 GoogLeNet","uri":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/"},{"categories":null,"content":"GoogLeNet 简介 GoogLeNet是2014年Christian Szegedy提出的一种全新的深度学习结构，在这之前的AlexNet、VGG等结构都是通过增大网络的深度来获得更好的训练效果，但层数的增加会带来很多负作用，比如过拟合、梯度消失、梯度爆炸等。inception的提出则从另一种角度来提升训练结果：更高效的利用计算资源，在相同的计算量下能提取到更多的特征，从而提升训练结果。 前代网络缺陷 ​ LeNet，AlexNet，VGG这些串联网络通过增大网络的深度来获得更好的训练效果，主要存在以下缺陷： 提取的特征图尺度单一。串联网络中，每一层级的卷积核都是固定尺寸的，只能提取固定尺度的特征。基于这种尺度单一的特征图构建的模型鲁棒性不强，泛化能力差。 参数量巨大，且难以将梯度传递到网络顶层。虽然多层小卷积核堆叠取得大卷积核能够减少参数量，但杯水车薪，网络越深也越容易发生梯度消失，使得网络难以训练。 解决方案及亮点 使用1x1的卷积来进行升降维。1×1卷积核的重要作用：降维或升维、跨通道信息交融、减少参数量、增加模型深度并提高非线性表达能力。 与直觉相一致，即视觉信息应该在不同的尺度上进行处理再聚合。 引入稀疏特性、将全连接层换成稀疏连接，利用稀疏矩阵分解成密集矩阵计算的原理来加快收敛速度： 空间（spatial）维度，只对图像的某一部分进行卷积，而不是对整个图像进行卷积。 特征（feature）维度，在多个尺寸上进行卷积再聚合，把相关性强的特征聚集到一起，每一种尺寸的卷积只输出256个特征中的一部分。 各版本inception改进 inception-v1 将多尺度的卷积层、池化层提取的特征图拼接输入下一层，提升模型多尺度特征提取能力； 利用1*1卷积对输入通道降维，减少模型参数量，并引入更多的非线性，提升模型泛化能力。 inception-v2 将Inception-v1中的大尺寸卷积核替换为多个小尺寸卷积核的堆叠，减少模型参数量，增加网络深度，提升模型拟合复杂分布的能力； 如果在训练模型时，每一批次的训练样本分布差别较大，那么网络的每一层都要重新去适应学习新的数据分布，这非常影响网络的训练速度，因此Inception-v2对卷积层进行了BatchNormalizatioin，有效地加速了网络训练过程，也能够消除梯度弥散； 引入了辅助分类器，即在靠近输出层的层级上引入旁路分类器，来解决梯度无法反向传递非常深的问题，辅助分类器只在训练时使能，测试时不开启辅助分类器； 结合了对称卷积与非对称卷积，增加了网络宽度。 inception-v3 inception-v3的改进基于以下几点优化思路： 避免特征表示瓶颈。尤其在网络前端，要避免严重压缩导致的瓶颈：特征表示尺寸应该从输入到输出端温和地减少，避免特征信息大量丢失。 高维信息更适合在网络的局部处理。在卷积网络中逐步增加非线性激活函数响应可以解耦更多的特征，加速网络训练。 空间聚合可以通过低维嵌入，不会导致网络表示能力的下降。在进行大尺寸卷积之前，在空间聚合前先对输入信息进行降维处理，可以加速训练。 平衡好网路的深度和宽度。通过平衡网络每层滤波器的个数和网络的层数可以使网络达到最佳性能。并行增加网络的深度和宽度时，模型性能提升最大。 改进点如下： 优化算法使用RMSProp替代SGD。 使用Label Smoothing Regularization方法。LSR是一种通过在输出y中加入噪声，对模型进行约束，降低模型过拟合的方法。 将大尺寸卷积核替换成小尺寸卷积核。 对辅助分类器中的全连接层进行BatchNormalization。 inception-v4 Inception-v4基本沿袭了Inception-v2/v3的设计，它的各个模块在结构上更加统一。 inception-ResNet Inception-ResNet借鉴了何凯明的残差连接思想，将Inception模块与残差连接进行结合。残差连接是指浅层特征通过另外一条支路直接加到高层特征中，达到特征复用的目的，可以有效避免深层网络的梯度弥散问题。 ","date":"2022-08-20","objectID":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/:3:0","tags":null,"title":"AlexNet \u0026 VGG \u0026 GoogLeNet","uri":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/"},{"categories":null,"content":"Self-thinking 提高深度神经网络性能方法 增加网络大小，包括深度和宽度，这是最直接的办法，也是最简单最安全的办法，尤其是当给定大量带标签的训练数据时。但是缺点明显：增加网络中训练的参数（导致过拟合），增加计算量。 上述问题的解决方案 从全连接层或卷积层完全转变到稀疏连接的结构。Arora等人的开创性工作表明如果数据集的概率分布可以用一个大的、非常稀疏的深度神经网络来表示，那么可以通过分析最后一层激活函数的相关统计量（均值方差等）和聚合有着高度相关输出的神经元来逐层生成最优的网络拓扑，这与赫布原理相仿——神经元一起发射且连接在一起。 Inception系列引发的思考（好的深度网络设计原则） 逐层构造网络。如果数据集的概率分布能够被一个神经网络所表达，那么构造这个网络的最佳方法是逐层构筑网络，即将上一层高度相关的节点连接在一起。几乎所有效果好的深度网络都具有这一点，不管AlexNet，VGG堆叠多个卷积，googLeNet堆叠多个inception模块，还是ResNet堆叠多个resblock。 稀疏的结构。人脑的神经元连接就是稀疏的，因此大型神经网络的合理连接方式也应该是稀疏的。稀疏的结构对于大型神经网络至关重要，可以减轻计算量并减少过拟合。 卷积操作（局部连接，权值共享）本身就是一种稀疏的结构，相比于全连接网络结构是很稀疏的。 符合Hebbian原理。Cells that fire together, wire together. 一起发射的神经元会连在一起。 相关性高的节点应该被连接而在一起。 而inception中 1×1的卷积恰好可以融合以上三者。一层可能会有多个卷积核，在同一个位置但在不同通道的卷积核输出结果相关性极高。一个1×1的卷积核可以很自然的把这些相关性很高，在同一个空间位置，但不同通道的特征结合起来。而其它尺寸的卷积核（3×3，5×5）可以保证特征的多样性，因此也可以适量使用。 总结 运用赫布原理和多尺度并行处理：从FC层转变到稀疏结构。 Inception模块的集成：使用1×1卷积核进行降维再用5×5卷积核升维。一个googlenet里包含九个Inceptionv1模块。 全局平均池化替代FC：减少参数量。做迁移学习更方便，用在弱监督学习和半监督学习中非常的可靠。 ","date":"2022-08-20","objectID":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/:4:0","tags":null,"title":"AlexNet \u0026 VGG \u0026 GoogLeNet","uri":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/"},{"categories":null,"content":"Citation AlexNet论文阅读总结及代码 VGG网络详解 一文读懂VGG网络 GoogLeNet论文翻译及解读 深度学习-GoogLeNet Thanks for reading! ","date":"2022-08-20","objectID":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/:5:0","tags":null,"title":"AlexNet \u0026 VGG \u0026 GoogLeNet","uri":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/"},{"categories":null,"content":"经典篇 Visual Perception (视觉感知) Characterizing edges (特征边缘)：边缘是指图像强度函数中急剧变化的位置。 图像滤波：在尽量保留图像细节特征的条件下对目标图像的噪声进行抑制，是图像处理中不可缺少的操作，其处理效果的好坏将直接影响到后续图像处理和分析的有效性和可靠性。 canny边缘检测算法： 用高斯滤波器对图像进行平滑处理； 利用一阶偏导算子找到图像灰度沿水平方向和垂直方向的偏导数，并求出梯度的幅值和方位； 非极大值抑制，找到局部梯度最大值； 用双阈值算法检测和链接边缘，凡大于高阈值T1的一定是边缘，凡小于低阈值T2的一定不是边缘，若既大于低阈值又小于高阈值，则要看这个像素的邻接像素中是否有大于高阈值的边缘像素，若有则是边缘，否则不是。 Feature Extraction (特征提取) Bag of Words, BoW (词袋模型) ","date":"2021-08-13","objectID":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/:1:0","tags":null,"title":"新一代视觉感知综述","uri":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/"},{"categories":null,"content":"现代篇 CNN (卷积神经网络) Vision Transformer (视觉自注意力网络) ","date":"2021-08-13","objectID":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/:2:0","tags":null,"title":"新一代视觉感知综述","uri":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/"},{"categories":null,"content":"前沿篇 XAI (可解释性AI) Contrastive Pretraining (对比预训练） Multimodal Fusion (多模态融合） ","date":"2021-08-13","objectID":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/:3:0","tags":null,"title":"新一代视觉感知综述","uri":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/"},{"categories":null,"content":"RetinaNet 简介 RetinaNet 是 Tsung-Yi Lin 和 Kaiming He（四作） 于 2018 年发表的论文 Focal Loss for Dense Object Detection。深入分析了极度不平衡的正负（前景背景）样本比例导致 one-stage 检测器精度低于 two-stage 检测器，基于上述分析，提出了一种简单但是非常实用的 Focal Loss 焦点损失函数，并且 Loss 设计思想可以推广到其他领域，同时针对目标检测领域特定问题，设计了 RetinaNet 网络，结合 Focal Loss 使得 one-stage 检测器在精度上能够达到乃至超过 two-stage 检测器。 网络结构 RetinaNet的特征提取网络选择了残差网络ResNet，特征融合这块选择了FPN（特征金字塔网络），以特征金字塔不同的尺寸特征图作为输入，搭建三个用于分类和框回归的子网络。分类网络输出的特征图尺寸为（W,H,KA)，其中W、H为特征图宽高，KA为特征图通道，存放A个anchor各自的类别信息（K为类别数）。 历史问题 在One stage中，detector直接在类别不平衡（负样本很多，正样本很少）中进行分类和回归，直接输出bbox和类别，原有的交叉熵损失无法处理这种不平衡，导致训练不充分，精度低，但是却提升了检测速度。 在Two stage中，FPN网络已经过滤了一部分的背景bbox，因此在fast r-cnn中正负样本比例较均衡，因此准确率较高。 针对所有的负样本，数量过多，主导了损失函数，不利于模型收敛。 针对单个负样本，大多数负样本不包含任何物体，属于易分样本，且易分样本数量很多，训练时对应背景类的预测得分会很高，那么单个样本的loss就很小，反向计算时梯度小，造成易分负样本对loss的收敛作用有限。 主要贡献 提出Focal Loss：解决one-stage算法中，样本不平衡和难易样本的问题。 样本不平衡：保证在损失函数中，正样本与负样本的贡献（比重）均衡。 难易样本：希望模型更关注难分样本，难分样本在loss中的比重更大。 Focal Loss 二分类交叉熵损失函数： 解决正负样本不均：加上一个权重α（范围[0,1]） 解决难易样本不均：引入因子γ(≥0)，将高置信度的样本的损失权重降低 Focal Loss 展开后为 优缺点分析 优点：提升准确率，降低了正负样本和难易样本不均衡带来的影响； 缺点：Focal Loss易受噪声干扰，对图像标注的准确性要求非常高，一旦有标错的样本，就会被focal loss当做困难样本，从而影响学习效果。 ","date":"2021-05-27","objectID":"/posts/retinanet+ampamp+fcos/:1:0","tags":null,"title":"RetinaNet \u0026 FCOS","uri":"/posts/retinanet+ampamp+fcos/"},{"categories":null,"content":"FCOS 简介 FCOS 发表于ICCV 2019，是一阶段anchor free目标检测算法，其主要的卖点为无锚。通过回归特征图上每个位置距离目标框的上下左右距离来实现目标检测。如果一个位置落在了多个目标框内，文中的方法是通过多尺度+回归幅度限制的方法来缓解这个问题。为了解决目标框数量过多的问题，文中提出了center-ness的方法，为每个位置学习一个center-ness分数，最后乘以预测类别分数作为非极大抑制的输入参数来解决这个问题。 核心思想 将铺设锚框变为铺设锚点，进行物体检测。 所谓铺设锚框，又称为Anchor-Based，是指在输出特征图上的每个像素的位置，放置几个预先定义的anchor框，在网络训练过程中，对这些anchor框进行分类与回归。 通过GT框和这些anchor框计算IoU，依据设定的阈值条件来定义正负样本，典型的有YOLOv3。 所谓铺设锚点，又称为Anchor-free，如下图所示，将原有的 对锚框进行分类与回归，变为对锚点进行分类与回归，其中回归是预测锚点到检测（GT）框上下左右四条边界的距离，典型的有FCOS。 Anchor-Based缺点 Anchor-Based方式，检测性能对于anchor的大小、数量、长宽比 都非常敏感，通过改变这些超参数Retinanet在COCO benchmark上面提升了4%的MAP。 固定size和aspect ratio的anchor损害了检测器的普适性，导致对于不同任务，anchor可能需要重新设置大小和长宽比。 为了达到更高的召回率（查全率），需要生成大量的anchor（FPN约18万个），但是大部分的anchor在训练时标记为负样本（negative），造成了样本不均衡问题。 在训练中，需要计算所有anchor与真实框的IoU，这样会消耗大量内存和计算资源。 FCOS优点 检测问题被统一到 FCN-solvable 的问题，可以简单地重用其他任务的idea，如语义分割。 anchor-free方式，不需要像anchor-based那样大量调整参数，使训练更为简单。 由于不需要计算IoU，节省了大量算力和内存。 提出了一些关于交叠区域的解决方法和思考。 模型部署会受到两种限制，一种是计算量的限制，一种是I/O 带宽的限制。anchor-free方式相比于anchor-based方式，对部署更友好一些。 网络结构 FCOS采用FPN结构，backbone的C3、C4、C5特征层作为FPN的输入，FPN生成P3、P4、P5、P6、P7特征图，送入后续的检测头Head。 每个Head包含3个分支： classification分支：预测类别，图中的C表示类别数，相当于C个二分类； regression分支：回归位置，图中的4表示：l、t、r、b，预测锚点到检测框上下左右四条边界的距离； center-ness：中心度，一个锚点对应一个中心度，用于锚点相对于检测框中心性的判断 在检测子网络Head中，分类分支和回归分支都先经过了4个卷积层进行了特征强化。 早期版本，在分类分支中，既包含 正、负样本锚点的 类别预测分支，又包含正、负样本锚点中心性判断的center-ness分支，用来强化检测结果； 回归分支用来回归正样本锚点到检测框上、下、左、右四个边界的距离 。 损失函数 ","date":"2021-05-27","objectID":"/posts/retinanet+ampamp+fcos/:2:0","tags":null,"title":"RetinaNet \u0026 FCOS","uri":"/posts/retinanet+ampamp+fcos/"},{"categories":null,"content":"Citation RetinaNet 一阶段目标检测器-RetinaNet网络详解 目标检测FCOS的初步理解 FCOS理论知识讲解 Thanks for reading! ","date":"2021-05-27","objectID":"/posts/retinanet+ampamp+fcos/:3:0","tags":null,"title":"RetinaNet \u0026 FCOS","uri":"/posts/retinanet+ampamp+fcos/"},{"categories":null,"content":"RCNN 简介 R-CNN的全称是Region-CNN，是第一个成功将深度学习应用到目标检测上的算法。R-CNN基于卷积神经网络，线性回归，和支持向量机等算法，实现目标检测技术。R-CNN遵循传统目标检测的思路，同样采用提取框，对每个框提取特征、图像分类、 非极大值抑制四个步骤进行目标检测。只不过在提取特征这一步，将传统的特征（如 SIFT、HOG 特征等）换成了深度卷积网络提取的特征。 算法流程 候选区域生成 使用Selective Search算法对每张输入图像使用选择性搜索来选取多个高质量的候选区域（Region Proposal）。这个算法先对图像基于像素信息做快速分割来得到多个区域，然后将当下最相似的两区域合并成一个区域，重复进行合并直到整张图像变成一个区域。最后根据合并的信息生成多个有层次结构的提议区 域，并为每个提议区域生成物体类别和真实边界框。 特征提取 选取一个预先训练好的卷积神经网络，去掉最后的输出层来作为特征抽取模块。对每个提议区域，将其变形成卷积神经网络需要的输入尺寸后进行前向计算抽取特征。 SVM分类器 将每个提议区域的特征连同其标注做成一个样本，训练多个支持向量机(SVM)来进行物 体类别分类，这里第 i 个 SVM 预测样本是否属于第 i 类。 边界框回归器 在这些样本上训练一个线性回归模型来预测（精修）真实边界框。对于SVM分好类的候选区域做边框回归，用Bounding box回归值校正原来的建议窗口，生成预测窗口坐标 Selective Search Selective Search（选择性搜索）是用于目标检测的region proposal算法，它计算速度快，具有很高的召回率，基于颜色，纹理，大小和形状兼容计算相似区域的分层分组。 图像中区域特征比像素更具代表性，作者使用Felzenszwalb and Huttenlocher的方法产生图像初始区域，使用贪心算法对区域进行迭代分组： 计算所有邻近区域之间的相似性； 两个最相似的区域被组合在一起； 计算合并区域和相邻区域的相似度； 重复2、3过程，直到整个图像变为一个地区。 在每次迭代中，形成更大的区域并将其添加到区域提议列表中。以自下而上的方式创建从较小的细分segments到较大细分segments的region proposal。 Bounding box regression Rcnn的Bounding box regression（边框回归）是指对粗略的预测框P进行平移和尺度放缩来微调， 使得经过微调后的窗口G’跟Ground Truth G更接近， 这样定位会更准确，其详细数学原理参考边框回归(Bounding Box Regression)详解。 ","date":"2020-09-21","objectID":"/posts/rcnn+ampamp+fast-rcnn+ampamp+faster-rcnn/:1:0","tags":null,"title":"Rcnn \u0026 Fast-Rcnn \u0026 Faster-Rcnn","uri":"/posts/rcnn+ampamp+fast-rcnn+ampamp+faster-rcnn/"},{"categories":null,"content":"Citation 边框回归(Bounding Box Regression)详解 理解Selective Search Thanks for reading! ","date":"2020-09-21","objectID":"/posts/rcnn+ampamp+fast-rcnn+ampamp+faster-rcnn/:2:0","tags":null,"title":"Rcnn \u0026 Fast-Rcnn \u0026 Faster-Rcnn","uri":"/posts/rcnn+ampamp+fast-rcnn+ampamp+faster-rcnn/"}] \ No newline at end of file +[{"categories":["Agent"],"content":"将推理（Reson）和行动（Acting）相结合，使得语言模型能够处理多种语言推理和决策任务。","date":"2023-10-03","objectID":"/posts/react/","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"ReAct: 语言模型中推理和行为的协同作用 虽然大型语言模型（LLM）在语言理解和交互式决策方面的任务中表现出印象深刻的表现，但推理能力（例如思维链提示）和动作能力（例如动作规划生成）主要作为单独的主题进行研究。本文探讨LLM的使用，以插入的方式生成推理跟踪和特定任务的动作，从而在两者之间实现更大的协同：推理跟踪有助于模型诱导、跟踪和更新动作规划并处理异常，而动作与外部来源（如知识库或环境）交互并收集其他信息。作者取名ReAct的方法应用于各种语言和决策任务，证明其除了提高可解释性和可信度以外，还在有效性方面超越SOTA基线方法。具体来说，在问答（HotpotQA）和事实验证（Fever）上，ReAct通过与简单的维基百科API交互，克服了思维链推理中常见的幻觉和错误传播问题，并生成类似人类的任务解决轨迹，比没有推理痕迹的基线更具可解释性。此外，在两个交互式决策基准（ALFWorld和WebShop）上，ReAct的性能优于模仿和强化学习方法，绝对成功率分别为34%和10%，其中只有一两个上下文示例提示。 如图所示：（1）比较4种提示方法，(a).标准方法、(b).思维链（CoT，仅推理）、(c).仅动作、(d).ReAct（推理+动作）等，解决HotpotQA问题；（2） 比较2个提示方法，(a). 仅动作、和 (b). ReAct，去解决 一个AlfWorld 游戏。在这两个方法中，省略提示中的上下文示例，仅显示模型（Act，Thought）和环境（Obs）生成的任务解决轨迹。 ","date":"2023-10-03","objectID":"/posts/react/:1:0","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"1 简介 此前LLM的推理能力(如思维链提示)和行动能力(如行动计划生成) 是作为单独的主题进行研究的。 本文探索了使用LLM以交错的方式生成推理轨迹和用于特定任务的动作，允许两者之间有更大的协同作用。 在问答（HotpotQA）和事实验证（Fever）方面，ReAct通过与简单的维基百科API交互，克服了思维链推理中普遍存在的幻觉和错误传播问题，并产生了类似人类的任务解决轨迹，比没有推理轨迹的基线更容易解释。 ","date":"2023-10-03","objectID":"/posts/react/:1:1","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"2 思路 人类智能的一个独特特征是能够将任务导向的行为与言语推理无缝结合，这在人类认知中起着重要作用，可以实现自我调节或策略化。 以在厨房里烹饪一道菜为例。在任何两个具体动作之间，我们可以用语言进行推理，以跟踪进度： “现在所有的东西都切好了，我应该把锅里的水加热”， 根据情况处理异常或调整计划： “我没有盐，那就让我用酱油和胡椒代替吧”， 并意识到什么时候需要外部信息： “我怎么准备面团?让我上网搜索一下”。 我们也可以采取行动： 打开烹饪书阅读食谱，打开冰箱，检查食材 来支持推理和回答问题： “我现在可以做什么菜?”。 这种“表演”和“推理”之间的紧密协同，即使是在看不见的情况下或面临信息不确定性的情况下，也能使人类快速学习新任务，并执行稳健的决策或推理。 ","date":"2023-10-03","objectID":"/posts/react/:1:2","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"3 方法 ReAct提示大型语言模型为给定任务生成口头推理历史步骤和操作。这些提示由少量的上下文示例组成，这些示例指导模型的思考和操作生成。下面的图中给出了一个上下文示例。这些例子引导代理经历一个循环过程:产生一个想法，采取一个行动，然后观察行动的结果。通过结合推理跟踪和操作，ReAct允许模型执行动态推理，这样可以生成高级计划，还可以与外部环境交互以收集额外的信息。 ","date":"2023-10-03","objectID":"/posts/react/:1:3","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"4 相关工作 针对强调推理的任务，ReAct会交替生成推理路径和动作，形成多个“推理-动作-观测”步骤。与之相反，对于偏重决策的任务，可能只在有限的步骤内进行推理，因此推理路径在提示中会呈现出稀疏的模式，并由语言模型自主决定是否以异步方式进行推理和动作。 推理与动作之间的协同作用使模型能够通过状态推理来建立、维护和调整动作计划（推理→动作），同时还能够与外部环境（如维基百科）进行互动，在推理过程中参考额外信息（动作→推理）。 使用LLM进行推理的最著名的工作是思维链(Chain-of-Thought，CoT)。它揭示了LLM制定自己的“思维过程”来解决问题的能力。 此后进行了几项后续工作，包括解决复杂任务的最小到最大（least-to-most）提示，zero-shotCoT(不需要任何训练数据,只需要输入问题,模型就可以生成解决这个新问题的CoT思维链)和自一致性推理（ self-consistency）()。 最近，Madaan \u0026 Yazdanbakhsh 系统地研究了CoT的形成和结构，并观察到符号、模式和文本的存在对CoT的有效性至关重要。其他工作也被扩展到更复杂的推理架构，而不是简单的提示。 与这些方法相比，ReAct执行的不仅仅是孤立的、固定的推理，还将模型操作及其相应的观察集成到一个连贯的输入流中，以便模型更准确地推理并处理推理之外的任务(例如交互式决策)。 ","date":"2023-10-03","objectID":"/posts/react/:1:4","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"5 应用及结果 研究人员将ReAct应用于多种语言推理和决策任务，包括问题回答、事实验证、基于文本的游戏和网页导航。结果是非常好的，ReAct在可解释性和可信赖性方面始终优于其他最先进的基线。 在问答和事实验证任务中，ReAct通过与简单的Wikipedia API交互，克服了推理中普遍存在的幻觉和错误传播问题。它生成了类似人类的解决任务的步骤，比没有推理痕迹的基线更容易解释。在交互式决策基准中，ReAct的表现明显优于模仿和强化学习方法，即使只有一两个上下文示例。 虽然推理、行动和观察步骤交织在一起提高了ReAct的接地性和可信度，但这种结构也约束降低了其制定推理步骤的灵活性，导致在某些任务上的推理错误率高于思维链提示。 ","date":"2023-10-03","objectID":"/posts/react/:1:5","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"6 结论 ReAct是一种简洁而高效的方法，能够在语言模型中协同推理和行动。它证明了将模型的推理能力、动作生成以及与外部环境的反馈整合到语言模型中是可行的。这也为语言模型在处理涉及与外部环境交互的任务时充当代理提供了机会。 ","date":"2023-10-03","objectID":"/posts/react/:1:6","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Agent"],"content":"7 引用 REACT: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS Thanks for reading!","date":"2023-10-03","objectID":"/posts/react/:1:7","tags":["LLM"],"title":"【论文阅读】ReAct","uri":"/posts/react/"},{"categories":["Emergent Abilities"],"content":"量变引起质变：涌现的能力指在小模型时并不具有，表现接近随机，而在模型规模越过一个阈值时，表现突然提升","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"大语言模型涌现的新能力 语言模型是根据已知文本生成未知文本的模型。自GPT-3以来，大型语言模型展现出了惊人的zero-shot和few-shot能力，即不改变参数仅改变输入的in-context learning。这是与此前流行的finetune范式截然不同的新范式。近期的ChatGPT，更是让文本生成从以前人们眼中的玩具，逐渐展现出了生产力工具的潜质。 本文展现了语言模型的增大（scale up）并非只是提高了一两个点的指标，而是发生了质变，突变式地拥有了小语言模型所不具有的新能力。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:0","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"1 涌现 涌现（emergence）或称创发、突现、呈展、演生，是一种现象，为许多小实体相互作用后产生了大实体，而这个大实体展现了组成它的小实体所不具有的特性。 水分子聚集后组成了雪花是一个物理上的创发现象 涌现可以用一句经典的话来概括：量变引起质变。 智能就是一种涌现现象，我们很了解单个神经元的电生理反应，但智能并不蕴含于单个神经元之中。而当大量的神经元相互交互，“智能”却在其中产生了。整体并不简单等于部分之和，其会具有组分所不具有的新功能。涌现有时是很难解释的，毕竟如果我们能解释清楚神经元是如何涌现出的智能，人工智能也就完成大半了。 具体到语言模型上，涌现的能力指在小模型时并不具有，表现接近随机，而在模型规模越过一个阈值时，表现突然提升。 扩大（Scaling up）语言模型已被证明可以预测性地提高各种下游任务的性能和样本效率。 样本效率（Sample efficiency）是指学习算法在使用尽可能少的训练样本的情况下，在某个任务上获得良好表现的能力。换句话说，它衡量了算法在学习任务时需要多少数据才能有效地学习。 一个样本效率高的算法可以使用较少的样本学习与一个样本效率低的算法相同的任务。这在获取更多的训练数据可能很困难或昂贵的情况下尤为重要，例如医学诊断或机器人领域。 样本效率受多种因素的影响，包括任务的复杂性、训练数据的质量和相关性以及算法的设计和容量。一些学习算法由于其能够从有限的数据中很好地推广，因此比其他算法更具有样本效率；而其他算法需要更多的数据才能获得良好的性能。 因此，在评估和比较不同的学习算法时，样本效率是一个重要的指标。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:1","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"2 大语言模型的新能力 为了观察新能力是如何在模型规模跨过阈值时产生的，首先我们需要有衡量模型规模的指标：使用训练FLOPs和模型参数量是很自然的想法。需要注意的是，我们无法用一个指标来衡量所有影响模型的因素。两个模型可能有同样的FLOPs，但有不同的模型参数量（如MoE）。此外，数据集的质量，优化的好坏也会影响模型的表现。这使得我们无法得到一个具体的涌现发生的规模阈值。 论文关注的涌现能力是模型的prompting（in-context learning）能力，这是大型语言模型最核心的能力。Prompting是指不需要通过训练改变模型参数，仅需在输入中添加文本（如对任务的描述），使模型在此基础上补充回答。一句话概括结论：prompting相关的能力是随着模型规模的增大而涌现的。下面将从few-shot prompting和augmented prompting strategies两方面进行介绍。 2.1 Few-shot Prompting Few-shot prompting是给出若干个输入-输出对作为prompt，模型需对新输入补充输出。这可能是目前最常见的prompting方法。 如下图所示，许多任务随着模型规模跨过阈值而可以被few-shot prompting解决，包括：加减乘除（A），基于知识的问答（G）等。有趣的一点是，在以训练FLOPs为规模指标时，各模型、各任务的涌现阈值均在 1022 量级左右。这就像生物只有大脑足够大才能有高级智能一样，许多高级能力只有在模型达到一定规模时才能获得。 2.2 Augmented Prompting Strategies 除了few-shot prompting以外，还有其他prompting或者finetuning策略可以进一步增加语言模型的能力。比如说对于涉及多步推理和计算的任务，如果让语言模型直接生成答案，往往效果不佳。但通过prompting时给出逐步思考的范例（chain of thought），抑或finetune模型来预测中间过程（scratchpad），都能大幅提高模型表现。 如下图所示，augmented prompting strategies同样只在模型规模跨过一定阈值后起正面作用，对于小模型甚至起负面作用。 大型语言模型的涌现能力列表，以及这些能力出现的规模： ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:2","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"3 关于Scaling的思考 3.1 更大 BIG-Bench是一个包含超过200个任务的用于评测语言模型的数据集，其中并非所有的任务都会发生涌现（如下图所示）。有的任务表现随scale up而平滑增加，而有的任务至今为止还没有通过scale up超过随机表现。比如同为算术类任务，simple arithmetic会平滑增加，modified arithmetic发生了涌现，multistep arithmetic还没有超过随机表现。 这些未解决的任务会是进一步研究的对象。这些问题是否能单纯靠scale up解决？涌现的原因是什么？涌现之后scale up是否有性能上限？即使scale up在性能提升上不会遇到瓶颈，计算的负载也会成为巨大的问题。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:3","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"3.2 更小 影响模型能力的不仅有模型的规模，还有数据、模型结构或是训练方法。在更好的数据、模型结构、训练方法下，我们可以在同样甚至更小的模型规模下实现更好的效果。 使用更好的数据可以在更低的模型规模下实现涌现：如PaLM 62B以更少的模型参数与更低的FLOPs，突破了LaMDA 137B和GPT-3 175B只能取得随机表现的多个任务。虽然因为高昂的训练成本，不可能进行详尽的消融实验，但一个可能的原因是PaLM使用了更好的数据（如多语言数据、代码数据）。数据本身可能也是涌现的原因，如数据中的长程依赖、稀有类别与few-shot prompting的涌现有关，chain of thought能力可能来自于代码数据。 更好的模型结构也可以降低涌现的阈值。如encoder-decoder模型要更适合于instruction finetuning。对于decoder模型，instruction finetuning仅对68B以上参数量的模型有效。但encoder-decoder模型仅需要11B。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:4","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"4 Discussion 我们已经看到，在少量样本提示设置或其他情况下，一系列能力到目前为止只在对足够大的语言模型进行评估时才被观察到。因此，它们的出现不能仅通过小型模型的表现简单外推来预测。具有涌现能力的少量样本提示任务也是不可预测的，因为这些任务并没有在预训练中显式包含，而且我们可能不知道语言模型可以执行的少量样本提示任务的全部范围。 这引发了一个问题，即进一步扩展是否会赋予更大的语言模型新的涌现能力。语言模型目前无法完成的任务是未来出现的主要候选对象；例如，在 BIG-Bench 中有数十个任务，即使是最大的 GPT-3 和 PaLM 模型也无法实现高于随机的性能。 BIG-Bench 是一个评估语言模型能力的广泛基准（benchmark），由 AI2、微软和卡内基梅隆大学等机构合作开发。它涵盖了来自多个领域的70个任务，包括自然语言理解、常识推理、知识库问答等等。这些任务旨在测试语言模型在大规模、复杂、多样化的应用场景下的表现，是目前最具挑战性的语言模型测试集之一。BIG-Bench 的任务数量和难度要远高于其他常见的语言模型基准，它的推出对于评估和推动语言模型的发展具有重要意义。 模型大小并不是解锁涌现能力的唯一因素。随着训练大型语言模型的科学的进步，对于具有新体系结构、更高质量数据或改进的训练过程的较小模型，某些能力可能会被解锁。一个例子是，InstructGPT、ChatGPT、GPT-4 模型提出了一种基于人类反馈的微调和强化学习方法（RLHF），这使得一个参数量 1.3B 的模型在广泛的用例中，在人类评估方面的表现优于更大的模型。 重要的是，风险也可能会出现，例如，大型语言模型的社会风险，如真实性、偏见和毒性。无论它们是否可以准确地被描述为“涌现”，这些风险都是重要的考虑因素，并且在某些情况下，随着模型规模的增加而增加。由于关于涌现能力的工作鼓励语言模型的规模扩大，因此重要的是要意识到随着模型规模的增加而增加的风险，即使它们不是涌现的。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:5","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"5 Conclusions 我们已经讨论了语言模型的涌现能力，迄今为止，只有在一定的计算规模上才观察到有意义的表现。涌现能力可以跨越各种语言模型、任务类型和实验场景。这些能力是最近发现的大型语言模型的结果，它们是如何出现的，以及更多的扩展是否会出现进一步的涌现能力成为 NLP 领域未来重要的研究方向。 ","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:6","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Emergent Abilities"],"content":"6 引用 Emergent Abilities of Large Language Models Thanks for reading!","date":"2023-09-14","objectID":"/posts/emergent_abilities_of_llm/:0:7","tags":["LLM"],"title":"【论文阅读】Emergent Abilities of Large Language Models","uri":"/posts/emergent_abilities_of_llm/"},{"categories":["Agent"],"content":"LLM+P将传统规划方法的优点整合到LLMs的框架中","date":"2023-09-05","objectID":"/posts/llm+p/","tags":["LLM"],"title":"【论文阅读】LLM+P","uri":"/posts/llm+p/"},{"categories":["Agent"],"content":"LLM+P：赋予大型语言模型规划能力 大语言模型(LLMs)已经展示了显著的零样本泛化能力，最先进的聊天机器人能够对日常生活中的常见问题给出合理的回答。然而，目前为止，LLMs并未能可靠地解决长期规划问题。相反，一旦以一种规范的方式给出问题，传统的规划方法就能够运用有效的搜索算法快速找到正确的，甚至是最优的解决方案。为了把这两者的优势结合起来，本文提出了LLM+P，这是第一个将传统规划方法的优点整合到LLMs的框架中。LLM+P接收一个用自然语言描述的规划问题，然后返回一个以自然语言表述的，解决该问题的正确（或最优）方案。LLM+P首先将语言描述转化为一个用规划领域定义语言（PDDL）编写的文件，然后利用传统规划方法快速找到解决方案，最后将找到的解决方案翻译回自然语言。此外，我们还定义了一组取自常见规划场景的基准问题。通过在这些基准问题上进行全面的实验，我们发现LLM+P能够为大多数问题提供最优解决方案，而LLMs在大多数问题上却无法提供可行的方案。 ","date":"2023-09-05","objectID":"/posts/llm+p/:1:0","tags":["LLM"],"title":"【论文阅读】LLM+P","uri":"/posts/llm+p/"},{"categories":["Agent"],"content":"1 PDDL介绍 Planning Domain Definition Language (PDDL) 是一种用于描述人工智能规划问题的语言。它包括领域定义和问题定义两个部分。 领域定义描述了可能的动作和它们的效果，每个动作都有一些前提条件，这些条件必须满足才能执行该动作，以及每个动作的效果，描述了动作执行后世界的状态如何改变。 问题定义描述了一个具体的规划问题，包括初始状态和目标状态。初始状态描述了规划问题开始时世界的状态，而目标状态描述了我们希望达到的状态。 例如，一个简单的\"搬箱子\"问题可以这样描述： 领域定义： (define (domain move) (:requirements :strips) (:predicates (robot-at ?x) (box-at ?x) (goal-at ?x)) (:action move-robot :parameters (?from ?to) :precondition (robot-at ?from) :effect (and (not (robot-at ?from)) (robot-at ?to))) (:action move-box :parameters (?from ?to) :precondition (and (robot-at ?from) (box-at ?from)) :effect (and (not (box-at ?from)) (box-at ?to) (not (robot-at ?from)) (robot-at ?to))) ) 问题定义： (define (problem move-1) (:domain move) (:objects location1 location2 location3) (:init (robot-at location1) (box-at location2) (goal-at location3)) (:goal (and (box-at location3))) ) 这个例子中，机器人和箱子可以在不同位置移动，目标是将箱子移动到目标位置。 然后，我们可以使用规划器，如 Fast Downward，来求解这个问题。Fast Downward 是一个非常知名的规划器，它使用了一种名为\"因果图\"的数据结构来帮助搜索解决方案。Fast Downward 接受 PDDL 描述的问题作为输入，然后使用各种搜索策略和启发式方法来寻找一个解决方案，即一系列的动作序列，这些动作将系统从初始状态引导到目标状态。 PDDL的优点是可扩展性强，但处理连续动作或状态、不确定效果或不完全信息的能力有限。 ","date":"2023-09-05","objectID":"/posts/llm+p/:1:1","tags":["LLM"],"title":"【论文阅读】LLM+P","uri":"/posts/llm+p/"},{"categories":["Agent"],"content":"2 文章提出的方法 本节首先使用一个实际的例子探讨如何以PDDL的形式构建规划提示，然后介绍LLM+P方法。 2.1 用LLM写PDDL 虽然LLMs在规划（或者说长期推理）方面表现得不尽人意，但它们在描述和翻译文本输入方面，包括以PDDL格式重写规划提示，方面做得很好。直观上，我们可以将PDDL视为一种不同于英语的语言，所以将规划提示以PDDL的形式重写其实就是一项“机器翻译”的任务，这是LLMs擅长的领域。这里我们提供了一个由GPT-4写就，没有进行任何优化的PDDL问题文件示例。我们可以看到，生成的文件似乎遵循了正确的PDDL语法，但使用了一个虚构的谓词（empty），并且漏掉了b1在桌子上的初始条件。 2.2 上下文学习 LLMs已经展示出了在不进行微调的情况下进行上下文学习的能力。上下文学习，指的是LLMs仅通过对一些输入-标签对（示例）的条件化，就能执行未见过的下游任务的能力。这里我们提供了一对用自然语言和PDDL描述的问题，作为GPT-4的上下文。当我们将这个上下文与上述示例中的提示结合起来时，生成的PDDL问题文件可以直接由规划器解决。 2.3 大型语言模型 + 传统规划方法 (LLM+P) 在介绍了LLM将问题以PDDL编码和上下文学习的能力后，我们现在准备介绍所提出的LLM+P解决方案（见图1的底部）。具体来说，我们假设对于每个问题领域，一个人类专家可以提供一个领域描述（也就是说，动作的先决条件和效果），这个描述将对该领域中出现的所有问题实例起到固定的作用。虽然自动生成这个描述的问题是另一个有价值的研究课题，但在这项提出的工作中，我们假设这个描述是以一个PDDL领域文件的形式存在的。LLM+P方法可以直接应用为给机器人系统指定任务的自然语言接口。例如，假设我们想要一个机器人扮演酒保的角色来制作鸡尾酒。告诉它可以执行哪些动作是合理的，但让它自己推断如何最有效地利用一组要组合的配料来制作新的鸡尾酒。此外，我们假设该代理人提供了一个最小的示例，演示了在该领域中一个简单问题的PDDL形式是什么样的。接下来，代理人提供了一个新的（可能非常复杂的）问题（P）。然后，LLM利用上下文学习来推断出与P对应的PDDL问题文件。一旦生成了问题的PDDL文件，我们将其与提供的领域PDDL文件一起输入到任何经典的规划方法中，生成一个PDDL方案。最后，LLM将PDDL方案翻译回自然语言，完成了LLM+P的流程。 总结来说，LLM+P的假设是： 聊天机器人能够根据与人类用户的对话，知道何时触发LLM+P。 对于用户提出的问题，提供了一个领域的PDDL文件。 提供了一个简单问题的自然语言描述和其对应的PDDL文件。 ","date":"2023-09-05","objectID":"/posts/llm+p/:1:2","tags":["LLM"],"title":"【论文阅读】LLM+P","uri":"/posts/llm+p/"},{"categories":["Agent"],"content":"3 引用 LLM+P: Empowering Large Language Models with Optimal Planning Proficiency Thanks for reading!","date":"2023-09-05","objectID":"/posts/llm+p/:1:3","tags":["LLM"],"title":"【论文阅读】LLM+P","uri":"/posts/llm+p/"},{"categories":["一些奇怪的东西"],"content":"0行代码、1.5元成本，基于全世界最先进的对话ai模型ChatGPT搭建属于你自己的微信对话机器人！","date":"2022-12-12","objectID":"/posts/chatgpt/","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"0 写在前面 ChatGPT（Conversational Graph-based Pre-trained Transformer）是一种面向对话机器人的预训练Transformer模型，旨在通过训练海量的对话数据来提高机器人的语言理解能力。这是一种基于图模型的预训练Transformer，可以更好地理解上下文和句子之间的关系，并自动学习语言中的模式，以更好地回答用户的问题。 关于ChatGPT能做什么，参考：ChatGPT惊人语录大赏 ","date":"2022-12-12","objectID":"/posts/chatgpt/:1:0","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"0.0 准备材料 电脑 梯子 1.5元 ","date":"2022-12-12","objectID":"/posts/chatgpt/:1:1","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"0.1 最终效果 实现一个基于ChatGPT的微信聊天机器人，支持单人私聊和拉入微信群@它聊天，若是在本地电脑运行则电脑关机会导致机器人下线，运行在云服务器则能一直使用。 不想折腾？直接添加我制作的微信机器人：somewhom1 ","date":"2022-12-12","objectID":"/posts/chatgpt/:1:2","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"0.2 注意事项 部署完chatgpt到某一个微信后，不能在手机退出这个微信账号，否则会失效； 若要切换部署到其他微信账号，需要将扫码后 config.json 所在目录下生成的 storage.json 文件删除再重新运行扫码。 部署chatgpt的可执行程序不可关闭，否则机器人将失效，若想不间断服务，建议在云服务器上后台运行。 ","date":"2022-12-12","objectID":"/posts/chatgpt/:1:3","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"1 注册OpenAI账号 ","date":"2022-12-12","objectID":"/posts/chatgpt/:2:0","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"1.0 OpenAI官网 挂梯子，进入OpenAI官网，点击右上角SIGN UP，输入邮箱注册或通过谷歌或微软账号注册，直到输入手机号验证（由于OpenAI在中国不提供服务，需要一个国外的手机号接收验证码验证）。 ","date":"2022-12-12","objectID":"/posts/chatgpt/:2:1","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"1.1 SMS-Activate接收国外手机号验证码 挂梯子，进入SMS-Activate官网，注册账号，邮箱验证有点抽风，需要等一下。 账号注册好后在网页左侧搜索openai： 点击后可以看到最便宜的印度和印度尼西亚号码只需要7卢布（不到人民币1元）： 不过我建议租7.5卢布的马来西亚号码，反正我自己租印度号码没收到验证码。购买需要充值大约0.15美元： 大约是1.2元不到： 在openai注册时需要的手机号验证中填写所租号码的地区号和手机号进行验证，过一会儿会在sms-activate中收到验证码，如果收不到在20分钟内可以取消该手机号重新租一个： 填写收到的验证码，完成注册。 至此你可以登录openai在线试用它提供的所有ai服务，除了ChatGPT外还有ai绘画、文本生成、代码生成等，这些也很有趣。如果想在线体验ChatGPT，可以点击上方的 Try it 按钮。 ","date":"2022-12-12","objectID":"/posts/chatgpt/:2:2","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"2 部署ChatGPT到微信 ","date":"2022-12-12","objectID":"/posts/chatgpt/:3:0","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"2.0 简要介绍 本部署方式基于github上的一个开源项目，原作者代码仓：https://github.com/869413421/wechatbot 某位博主上传到gitee（不需梯子）的版本：https://gitee.com/shtml/wechatbot 项目基于openwechat 开源项目开发，代码仓：https://github.com/eatmoreapple/openwechat 作者提到的部署方式有两种： 支持Windows 和 Linux，但是本机需要有Golang环境。直接编译运行项目即可。 支持Windows 和 Linux，不需本地有Golang环境。下载可执行文件直接启动即可。简单无脑，下面的教程基于这种部署方式。 ","date":"2022-12-12","objectID":"/posts/chatgpt/:3:1","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"2.1 部署到本地 2.1.0 下载所需文件 有git的情况 进入准备安装的目录 git clone https://gitee.com/shtml/wechatbot.git 没git的情况 进入https://gitee.com/shtml/wechatbot/tree/main/bin下载config.json和一个可执行文件（这两个文件要放在同一个目录下），可执行文件参考电脑系统，386为32位系统，amd64为64位系统，.exe为windows系统的可执行文件，linux结尾为linux系统，64位windows系统下载wechatbot-amd64.exe。 2.1.1 配置config.json 挂梯子，再次进入https://beta.openai.com/overview，登录openai账号后，点击右上角账号： 再点击 View API Keys ，第一次需要点击Create new secret key创建API Keys： 创建好后复制这个key，进入之前存放config.json文件的目录进行配置： 可直接用记事本打开config.json，把apikey的内容改为之前复制的 API key ： auto_pass字段推荐改为 false ，这个字段为是否自动同意好友申请。 至此，配置完成。 2.1.2 运行程序 运行之前下载的可执行文件 wechatbot-amd64.exe： 若浏览器没有自动跳转就将下面的网址复制到浏览器打开，会出现一个二维码，用微信扫描完成登录，此时可执行文件所在目录会出现一个 storage.json文件 。 至此，微信对话机器人部署完成，你拥有了一个目前最先进的对话机器人，try it！ ","date":"2022-12-12","objectID":"/posts/chatgpt/:3:2","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"2.2 部署到云服务器 注：上述过程已经完成了一次在本地的部署，可供短期玩耍，但当本地电脑关机或程序关闭时机器人会失效，部署在云服务器上可解决这一问题，但这种方式需要一些简单的技术功底。 2.2.0 租云服务器 这里推荐阿里云服务器，新用户注册可以免费使用一个月：阿里云新人特惠链接 反正免费，推荐能选的配置越高越好，还可以折腾折腾别的，推荐租 ubuntu 系统的云服务器。 2.2.1 部署chatgpt到云服务器 进入用户目录： cd /home 拉取代码： git clone https://gitee.com/shtml/wechatbot.git 进入bin目录： cd wechatbot/bin 编辑config.json（配置与2.1.1相同）： vim config.json 创建screen会话： screen -S wechatbot 运行可执行文件： ./wechatbot-amd64-linux 将命令行中的链接复制到浏览器扫码登录，最后 Ctrl + a + d 将会话放于后台运行。 至此，云服务器上的ChatGPT微信机器人部署完成，你拥有了一个24小时工作的目前最先进的对话机器人，have fun！ 若登录后要切换微信，则需要删除 storage.json 文件： rm storage.json ","date":"2022-12-12","objectID":"/posts/chatgpt/:3:3","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["一些奇怪的东西"],"content":"3 引用 OpenAI ChatGPT 接入 个人WeChat(微信)，让AI互动更方便！ Thanks for reading!","date":"2022-12-12","objectID":"/posts/chatgpt/:4:0","tags":["NLP"],"title":"搭建ChatGPT微信对话机器人教程","uri":"/posts/chatgpt/"},{"categories":["Study Blog"],"content":"description","date":"2022-12-07","objectID":"/posts/hpe/","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"1 任务介绍 3D Human Pose Estimation（以下简称 3D HPE ）的目标是在三维空间中估计人体关键点的位置。3D HPE 的应用非常广泛，包括人机交互、运动分析、康复训练等，它也可以为其他计算机视觉任务（例如行为识别）提供 skeleton 等方面的信息。 近几年，随着深度学习在人体姿态估计领域的成功应用，2D HPE 的精度和泛化能力都得到了显著提升。然而，相较于 2D HPE，3D HPE 面临着更多的挑战。一方面，受数据采集难度的限制，目前大多数方法都是基于单目图像或视频的，而从 2D 图像到 3D 姿态的映射本就是一个多解问题。另一方面，深度学习算法依赖于大量的训练数据，但由于 3D 姿态标注的难度和成本都比较高，目前的主流数据集基本都是在实验室环境下采集的，这势必会影响到算法在户外数据上的泛化性能。另外，2D HPE 面临的一些难题（例如自遮挡）同样也是 3D HPE 亟待解决的问题。 ","date":"2022-12-07","objectID":"/posts/hpe/:1:0","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"2 表示方式 骨架关键点：以骨架的形式表示人体姿态，由一系列的人体关键点和关键点之间的连线构成； 参数化的人体模型：以mesh（网格）形式表示人体姿态和体型。 ","date":"2022-12-07","objectID":"/posts/hpe/:2:0","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"3 多人场景 多人场景的3D-HPE和2D-HPE类似，可分为top-down（自顶向下）和bottom-up（自底向上）两类。 top-down top-down 方法先利用目标检测算法确定人体的 bounding box，然后对于每一个 bounding box 中的人体，计算其根关节的绝对坐标以及其他关节相对于根关节的坐标。 bottom-up bottom-up 方法先预测所有关节的位置，再根据关节间的相对关系将属于同一个人的关节联系起来构成完整的人体。 Bottom-up 方法的主要优点在于它的运行时间基本不会受到待检测人体数量的影响，因此在拥挤场景下 bottom-up 方法更有优势。再者，bottom-up 方法的关键点定位是在整体图像的基础上完成的，而 top-down 方法则是在 bounding box 区域内完成的，因此 bottom-up 方法更有利于把握全局信息，从而能更准确地定位人体在相机坐标系下的绝对位置。 ","date":"2022-12-07","objectID":"/posts/hpe/:3:0","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"4 常用数据集 由于 3D 关键点标注难度较大，目前的数据集基本上都借助于 MoCap 和可穿戴 IMU设备来完成数据标注，也正因为此，大多数数据集都局限于室内场景。 ","date":"2022-12-07","objectID":"/posts/hpe/:4:0","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"4.1 Human3.6M Human3.6M 是目前基于多视图的 3D 人体位姿研究最为重要的一个数据集，包含了360万帧图像和对应的 2D/3D 人体姿态。该数据集在实验室环境下采集，通过4个高清相机同步记录4个视角下的场景，并通过 MoCap 系统获取精确的人体三维关键点坐标及关节角。如图所示，Human3.6M 包含了多样化的表演者、动作和视角。 Human3.6M 的评价指标主要有 Mean Per Joint Position Error (MPJPE) 和 P-MPJPE。其中，MPJPE 是所有关键点预测坐标与 ground truth 坐标之间的平均欧式距离，一般会事先将预测结果与 ground truth 的根关节对齐；P-MPJPE 则是将预测结果通过 Procrustes Analysis 与 ground truth 对齐，再计算 MPJPE。 ","date":"2022-12-07","objectID":"/posts/hpe/:4:1","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"4.2 MPI-INF-3DHP Human3.6M 尽管数据量大，但场景单一。为了解决这一问题，MPI-INF-3DHP 在数据集中加入了针对前景和背景的数据增强处理。具体来说，其训练集的采集是借助于多目相机在室内绿幕场景下完成的，对于原始的采集数据，先对人体、背景等进行分割，然后用不同的纹理替换背景和前景，从而达到数据增强的目的。测试集则涵盖了三种不同的场景，包括室内绿幕场景、普通室内场景和室外场景。因此，MPI-INF-3DHP 更有助于评估算法的泛化能力。 在评价标准方面，除了 MPJPE，该数据集也把 2D HPE 中广泛使用的 Percentage of Correct Keypoints (PCK) 和 Area Under the Curve (AUC) 扩展到了 3D HPE 中。其中 PCK 是误差小于一定阈值（150 mm）的关键点所占的百分比，AUC 则是 PCK 曲线下的面积。 ","date":"2022-12-07","objectID":"/posts/hpe/:4:2","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"4.3 Panoptic (CMU Panoptic Studio) CMU Panoptic [13] 是一个大型的多目图像数据集，提供了31个不同视角的高清图像以及10个不同视角的 Kinect 数据，包含了65段视频（总时长5.5 h），3D skeleton 总数超过150万。该数据集还包含了多个多人场景，因此也成为了多人 3D HPE 的 benchmark 之一。 CMU Panoptic 的评价标准与 Human3.6M 类似，一般采用 MPJPE 和 P-MPJPE 来评估算法精度。 ","date":"2022-12-07","objectID":"/posts/hpe/:4:3","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"5 主流算法 ","date":"2022-12-07","objectID":"/posts/hpe/:5:0","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"5.1 基于单目图像的方法 由于单目图像易于获取且不受场景限制，很多方法都以此为输入数据。但是根据 2D 图像估计 3D 姿态是一个不适定问题，即可能存在多个不同的 3D 姿态，它们的 2D 投影是同一个 2D 姿态。并且，基于单目图像的方法也面临着自遮挡、物体遮挡、深度的不确定性等问题。由于缺少 3D 信息，目前的方法大多只能预测 root-relative pose，即以根关节（pelvis）为坐标原点的三维姿态。 基于单目图像的方法可以根据是否依赖 2D HPE 分成两类，下面将分别介绍这两类方法。 5.1.1 直接预测 这类方法不依赖 2D HPE，直接从图像回归得到 3D 关键点坐标。代表作 C2F-Vol [3] 借鉴了 2D HPE 中的 Hourglass 网络结构，并以 3D Heatmap 的形式表示 3D pose。为了降低三维数据带来的巨大存储消耗，采用了在 depth 维度上逐渐提升分辨率的方法。 5.1.2 2D-to-3D Lifting 得益于 2D HPE 的高精度和泛化能力，许多方法选择以 2D HPE 作为中间步骤，根据 2D pose（和原始图像特征）去估计 3D pose。SimpleBaseline3D [4] 是其中的一种经典方法。该方法以 2D 关键点坐标作为输入，通过残差连接的全连接层直接将 2D pose 映射到 3D 空间。尽管模型非常简单，该算法在当时达到了 SOTA 水平，并通过实验证明了目前大多数 3D HPE 算法的误差主要来源于图像信息的理解（2D HPE）而不是 2D-to-3D lifting 过程。 由于上述算法仅以 2D pose 作为输入，因此高度依赖 2D pose 的准确性，一旦 2D HPE 失败，将会严重影响后续的 2D-to-3D lifting。为了解决这一问题，也有一些算法同时学习 2D 和 3D 姿态 [5]，这样一方面可以为 2D-to-3D lifting 引入来自于原始图像的信息，另一方面也为 2D/3D 数据集混合训练提供了可能，进一步提升算法的泛化能力。 ","date":"2022-12-07","objectID":"/posts/hpe/:5:1","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"5.2 基于多目图像的方法 为了解决遮挡问题，目前最有效的方法之一就是融合多视角信息。为了从多目图像中重建 3D pose，关键在于如何确定场景中同一个点在不同视角下的位置关系。一些方法引入多视角间的一致性约束，例如下图的方法：同时输入两个视角的图像，对于其中某一个视角的 2D pose 输入，根据两个视角间的转换关系，预测另一个视角的 3D pose 输出。也有一些方法借助于立体视觉中的 triangulation 方法，将所有视角下的 2D heatmap 聚合起来构成一个 3D volume，随后再通过三维卷积网络输出 3D heatmap。 结合多目图像可以帮助解决遮挡问题，并且也能够在一定程度上解决深度不确定问题。但是这类方法对于数据采集的要求比较高，且模型结构相对复杂，在实际场景中的应用具有一定的局限性。 ","date":"2022-12-07","objectID":"/posts/hpe/:5:2","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"5.3 基于视频的方法 基于视频的方法就是在以上两类方法的基础上引入时间维度上的信息。相邻帧提供的上下文信息可以帮助我们更好地预测当前帧的姿态。对于遮挡情况，也可以根据前后几帧的姿态做一些合理推测。另外，由于在一段视频中同一个人的骨骼长度是不变的，因此这类方法通常会引入骨骼长度一致性的约束限制，有助于输出更加稳定的 3D pose。 ","date":"2022-12-07","objectID":"/posts/hpe/:5:3","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["Study Blog"],"content":"5.4 其他方法 除了 RGB 图像以外，也有越来越多的传感器被应用于 3D HPE 任务中。常见的有深度相机、雷达、IMU（惯性测量单元）等。深度相机和雷达都提供了三维空间上的信息，这对于 3D HPE 来说非常重要，可以帮助缓解或消除深度不确定性问题。IMU 设备可以提供关节方向信息，它不依赖于视觉信号，因此可以帮助解决遮挡问题。 Thanks for reading!","date":"2022-12-07","objectID":"/posts/hpe/:5:4","tags":["HPE"],"title":"3D Human Pose Estimation","uri":"/posts/hpe/"},{"categories":["tast_cate"],"content":"description","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":["tast_cate"],"content":"Self-Supervised PreTrain ","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/:0:0","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":["tast_cate"],"content":"Pretext task Pretext task也叫surrogate task，也称作代理任务。 Pretext可以理解为是一种为达到特定训练任务而设计的间接任务。比如，我们要训练一个网络来对ImageNet分类，可以表达为 fθ(x): x→y，我们的目的其实是获得具有语义特征提取/推理能力的 θ。我们假设有另外一个任务（也就是pretext），它可以近似获得这样的θ，比如，Auto-encoder（AE），表示为：gθ(x): x→x。为什么AE可以近似 θ呢？因为AE要重建 x 就必须学习 x 中的内在关系，而这种内在关系的学习又是有利于我们学习 fθ(x) 的。这种方式也叫做预训练，为了在目标任务上获得更好的泛化能力，一般还需要进行fine-tuning等操作。 ","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/:1:0","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":["tast_cate"],"content":"MAE Fine-tuning linear probe linear probe不对训练好的模型做变动，只是用它从下游数据集提取特征，然后利用逻辑回归拟合标签； fine-tune fine-tune利用下游数据再次微调预模型中的所有参数。 ","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/:2:0","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":["tast_cate"],"content":"CAE (a)CAE；(b)BEiT；(c)DAE encoder 仅用来做表征，将patches映射到一个表征空间。 latent contextual regressor 用来通过使用encoder表征的可视patches来预测masked patches的表征，并添加一个alignment constraint来与使用encoder表征的masked patches进行对齐。 这一部分的作用使得CAE的encoder承担了全部的表征学习能力。 decoder 将masked patches的表征映射回targets。 ","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/:3:0","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":["tast_cate"],"content":"Contrastive Learning 典型的对比学习的任务是通过解决最大化来自同一图像的增强视图之间的相似度以及最小化来自不同图像的增强图像之间的相似度来预训练一个网络。 对于ImageNet数据集，典型的对比学习主要学到了图像中心关于这一千个分类的知识，而MIM方法能够学到非中心区域的、这一千个分类以外的知识。 Thanks for reading! ","date":"2022-11-20","objectID":"/posts/self-supervised+pretrain/:4:0","tags":["test_tag"],"title":"Self-Supervised PreTrain","uri":"/posts/self-supervised+pretrain/"},{"categories":null,"content":"FCN 简介 FCN（Fully Convolutional Networks，全卷积网络）是Jonathan Long等人于2015年在Fully Convolutional Networks for Semantic Segmentation一文中提出的用于图像语义分割的一种框架，是深度学习用于语义分割领域的开山之作。FCN将传统CNN后面的全连接层换成了卷积层，这样网络的输出将是热力图而非类别；同时，为解决卷积和池化导致图像尺寸的变小，使用上采样方式对图像尺寸进行恢复。 核心思想 不含全连接层的全卷积网络，可适应任意尺寸输入； 反卷积层增大图像尺寸，输出精细结果； 结合不同深度层结果的跳级结构，确保鲁棒性和精确性。 网络结构 FCN是一个端到端，像素对像素的全卷积网络，用于进行图像的语义分割。整体的网络结构分为两个部分：全卷积部分和上采样部分。FCN中，使用了vgg16的卷积部分作为backbone，并将vgg16的最后三个全连接层也改为卷积层。除此之外，还增加了上采样部分，这里是使用转置卷积进行上采样. 优缺点分析 优点 FCN网络可以实现端到端的预测，可以接受任意大小的输入图像尺寸（因为没有全连接层），比较高效。 局限性 得到的结果还是不够精细。进行8倍上采样虽然比32倍的效果好了很多，但是上采样的结果还是比较模糊的，对图像中的细节不敏感。而且在对各个像素进行分类时，没有考虑像素与像素之间的关系。 ","date":"2022-10-27","objectID":"/posts/fcn+ampamp+u-net/:1:0","tags":null,"title":"FCN \u0026 U-Net","uri":"/posts/fcn+ampamp+u-net/"},{"categories":null,"content":"U-Net 简介 U-Net最早发表在2015的MICCAI上。后成为大多做医疗影像语义分割任务的baseline，也启发了大量研究者去思考U型语义分割网络。在自然影像理解方面，越来越多的语义分割和目标检测SOTA模型开始关注和使用U型结构，比如语义分割Discriminative Feature Network(DFN)(CVPR2018)，目标检测Feature Pyramid Networks for Object Detection(FPN)(CVPR 2017)等。 网络结构 UNet 网络结构如上图所示，其网络结构是对称的，形似英文字母 U，故而被称为 UNet 。就整体而言，UNet 是一个Encoder-Decoder的结构（与 FCN 相同），前半部分是特征提取，后半部分是上采样。 Encoder：左半部分，由两个 3x3 的卷积层（ReLU）+ 一个 2x2 的 maxpooling 层组成一个下采样模块。由卷积操作和下采样操作组成，所用卷积结构统一为 3x3 的卷积核，padding=0，striding=1。没有 padding 所以每次卷积之后特征图的 H 和 W 变小了，在跳层连接（Skip connection）时需注意特征图的维度。 Decoder：右半部分，由一个上采样的卷积层 + 特征拼接 concat + 两个 3x3 的卷积层（ReLU）构成一个上采样模块。Decoder 用以恢复特征图的原始分辨率，除了卷积以外，该过程的关键步骤就是上采样与跳层连接。上采样常用转置卷积和插值两种方式实现。在插值实现方式中，双线性插值（bilinear）的综合表现较好也较为常见 。UNet 中的跳层连接通过拼接将底层的位置信息与深层的语义信息相融合。而在在 FCN 中，特征图是以相加的方式进行融合。 与FCN对比 FCN：通过特征图对应像素值的相加来融合特征。特征图维度没有变化，但每个维度包含了更多特征。对于普通分类任务这种不需要从特征图复原到原始分辨率的任务来说，这是一个高效的选择； UNet ：通过通道数的拼接，以形成更厚的特征（当然这样会更佳消耗显存）。拼接方式：保留了更多的维度和位置信息，这使得后面的网络层可在浅层特征与深层特征间自由选择，这对语义分割任务来说更具优势。 与FPN的对比 同： 都使用了“由上至下”、“横向连接”及“由下至上”的结构，从而对多尺度特征图进行融合，即将高层的语义信息与低层的几何细节结合。另外，融合后都会再经过一层卷积。 异： FPN对多尺度特征图融合的方式是element-wise add，而UNet采用的是concate； FPN对多尺度特征图都进行了预测，而UNet仅在（由上至下）最后一层进行预测，而且这一层通常还需要进行一次resize才能恢复到原图尺寸； FPN对高层特征图采用的放大方式是插值，而UNet通常还会使用转置卷积，通过网络自学习的方式来进行上采样； FPN的高层特征放大2倍后与低层的尺寸恰好一致，而在UNet中通常不一致，还需要对低层特征做crop使得与放大后的高层特征尺寸一致； FPN在下采样时的卷积带有padding，分辨率的下降仅由stirde决定，而UNet的卷积通常不带padding，使得分辨率下降在stride的基础上还会额外的减小。也就是说，FPN的“由下至上”和“由下至上”是对称结构，而UNet其实是非对称的，这也是导致4和2中最后提到的原因‘； FPN在特征层融合后经过一层卷积是为了消除上采样过程中产生的混叠效应带来的影响，而UNet中还起到了压缩通道的作用（也是由于UNet融合特征层时采用的是concate，因此需要压缩通道减少计算量）； FPN主要针对detection任务，而UNet针对segmentation任务，前者通常作为一个模块嵌入到网络结构中，而后者本身就是一种网络模型结构。 为什么适用于医学图像？ 因为医学图像边界模糊、梯度复杂，需要较多的高分辨率信息。高分辨率用于精准分割。 人体内部结构相对固定，分割目标在人体图像中的分布很具有规律，语义简单明确，低分辨率信息能够提供这一信息，用于目标物体的识别。 UNet结合了低分辨率信息（提供物体类别识别依据）和高分辨率信息（提供精准分割定位依据），完美适用于医学图像分割。 ","date":"2022-10-27","objectID":"/posts/fcn+ampamp+u-net/:2:0","tags":null,"title":"FCN \u0026 U-Net","uri":"/posts/fcn+ampamp+u-net/"},{"categories":null,"content":"Citation FCN网络解析 FCN（全卷积神经网络）详解 UNet 浅析 研习U-Net FPN与Unet的异同 Thanks for reading! ","date":"2022-10-27","objectID":"/posts/fcn+ampamp+u-net/:3:0","tags":null,"title":"FCN \u0026 U-Net","uri":"/posts/fcn+ampamp+u-net/"},{"categories":null,"content":"FPN 提出原因 卷积网络中，深层网络容易响应语义特征，浅层网络容易响应图像特征。然而，在目标检测中往往因为卷积网络的这个特征带来了不少麻烦：高层网络虽然能响应语义特征，但是由于Feature Map的尺寸太小，拥有的几何信息并不多，不利于目标的检测；浅层网络虽然包含比较多的几何信息，但是图像的语义特征并不多，不利于图像的分类。这个问题在小目标检测中更为突出。因此需要能够合并深层和浅层特征的网络，同时满足目标检测和图像分类的需要。 参考思想 FPN使用的是图像金字塔的思想。 传统的图像金字塔采用输入多尺度图像的方式构建多尺度的特征。输入一张图像后，可以通过一些手段获得多张不同尺度的图像，将这些不同尺度图像的4个顶点连接起来，就可以构造出一个类似真实金字塔的一个图像金字塔。整个过程有点像是我们看一个物品由远及近的过程（近大远小原理）。 其中，中间的图像是原始图像，尺寸越来越小的图片是经过下采样处理后的结果，而尺寸越来越大的图片是经过上采样处理后的结果。把高层的特征传下来，补充低层的语义，这样就可以获得高分辨率、强语义的特征，有利于小目标的检测。 特征金字塔 运用金字塔的思想可以提高算法的性能，但是需要大量的运算和内存。因此特征金字塔要在速度和准确率之间进行权衡，通过它获得更加鲁棒的语义信息。 图像中存在不同大小的目标，而不同的目标具有不同的特征，所以需要特征金字塔来利用浅层的特征将简单的目标区分开，利用深层的特征将复杂的目标区分开。即利用大的特征图区分简单目标，利用小的特征图区分复杂目标。 具体思路 图（a）： 先对原始图像构造图像金字塔，然后在图像金字塔的每一层提出不同的特征，然后进行相应的预测。 优点：精度较好。 缺点：计算量和占用内存太大。 图（b）： 通过对原始图像进行卷积和池化操作来获得不同尺寸的feature map，在图像的特征空间中构造出金字塔。因为浅层的网络更关注于细节信息，高层的网络更关注于语义信息，更有利于准确检测出目标，因此利用最后一个卷积层上的feature map来进行预测分类。 优点：速度快、内存少。 缺点：仅关注深层网络中最后一层的特征，却忽略了其它层的特征。 图（c）： 同时利用低层特征和高层特征。就是首先在原始图像上面进行深度卷积，然后分别在不同的特征层上面进行预测。 优点：在不同的层上面输出对应的目标，不需要经过所有的层才输出对应的目标（即对于有些目标来说，不用进行多余的前向操作），速度更快，又提高了算法的检测性能。 缺点：获得的特征不鲁棒，都是一些弱特征（因为很多的特征都是从较浅的层获得的）。 图（d）： FPN网络，对最底层的特征进行向上采样，并与该底层特征进行融合，得到高分辨率、强语义的特征（即加强了特征的提取）。 整体过程 自下而上：先把预处理好的图片送进预训练的网络，如ResNet，构建自下而上的网络，对应上图左侧金字塔。 自上而下：左侧顶层直接复制到右侧顶层，对右侧顶层进行上采样操作（就是2 * up），再用1 * 1卷积对左侧次顶层进行降维处理，然后将两者对应元素相加（这里就是高低层特征的一个汇总），后续以此类推，如此构成自上而下网络。 卷积融合：最后我们对右侧各层分别来一个3 * 3卷积操作得到最终的预测（对应上图的predict）。 ","date":"2022-10-26","objectID":"/posts/fpn+ampamp+ssd+ampamp+yolo/:1:0","tags":null,"title":"FPN \u0026 SSD \u0026 YOLO","uri":"/posts/fpn+ampamp+ssd+ampamp+yolo/"},{"categories":null,"content":"SSD 简介 SSD，全称Single Shot MultiBox Detector，是Wei Liu在ECCV 2016上提出的一种目标检测算法，截至目前是主要的检测框架之一，相比Faster RCNN有明显的速度优势，相比YOLO又有明显的mAP优势。 背景 目标检测主流算法分成两个类型： two-stage方法：RCNN系列 通过算法产生候选框，然后再对这些候选框进行分类和回归。 one-stage方法：yolo和SSD 直接通过主干网络给出类别位置信息，不需要区域生成。 特点 从YOLO中继承了将detection转化为regression的思路，一次完成目标定位与分类。 基于Faster RCNN中的Anchor，提出了相似的prior box。 加入基于特征金字塔（Pyramidal Feature Hierarchy）的检测方式，即在不同感受野的feature map上预测目标。 这些设计实现了简单的端到端的训练，而且即便使用低分辨率的输入图像也能得到高的精度。 网络结构 采用多尺度特征图用于检测 CNN网络一般前面的特征图比较大，后面会逐渐采用stride=2的卷积或者pool来降低特征图大小，这正如图3所示，一个比较大的特征图和一个比较小的特征图，它们都用来做检测。这样做的好处是比较大的特征图来用来检测相对较小的目标，而小的特征图负责检测大目标。 采用卷积进行检测 SSD直接采用卷积对不同的特征图来进行提取检测结果。对于形状为mnp的特征图，只需要采用33p这样比较小的卷积核得到检测值。（每个添加的特征层使用一系列卷积滤波器可以产生一系列固定的预测）。 设置先验框 SSD借鉴faster rcnn中ancho理念，每个单元设置尺度或者长宽比不同的先验框，预测的是对于该单元格先验框的偏移量，以及每个类被预测反映框中该物体类别的置信度。 模型结构 SSD的模型框架主要由三部分组成，以SSD300为例，有VGG-Base Extra-Layers，Pred-Layers。 VGG-Base作为基础框架用来提取图像的feature，Extra-Layers对VGG的feature做进一步处理，增加模型对图像的感受野，使得extra-layers得到的特征图承载更多抽象信息。待预测的特征图由六种特征图组成，6中特征图最终通过pred-layer得到预测框的坐标，置信度，类别信息。 ","date":"2022-10-26","objectID":"/posts/fpn+ampamp+ssd+ampamp+yolo/:2:0","tags":null,"title":"FPN \u0026 SSD \u0026 YOLO","uri":"/posts/fpn+ampamp+ssd+ampamp+yolo/"},{"categories":null,"content":"YOLO 简介 YOLO在2016年被提出，发表于CVPR。YOLO的全称是you only look once，指只需要浏览一次就可以识别出图中的物体的类别和位置。因为只需要看一次，YOLO被称为Region-free方法，相比于Region-based方法，YOLO不需要提前找到可能存在目标的Region。 具体思路 将一幅图像分成SxS个网格(grid cell)，如果某个object的中心落在这个网格中，则这个网格就负责预测这个object。每个bounding box要预测(x, y, w, h)和confidence共5个值，每个网格还要预测一个类别信息，记为C类。则SxS个网格，每个网格要预测B个bounding box还要预测C个categories。输出就是S x S x (5*B+C)的一个tensor，网络主干是GooleNet。 存在问题 损失函数中localization error和classification error同等重要（解决办法可以是：对没有object的box的confidence loss，赋予小的loss weight；只有当某个网格中有object的时候才对classification error进行更新）。 输出为全连接层，只支持与训练图像相同的输入分辨率。 YOLO对相互靠的很近的物体（挨在一起且中点都落在同一个格子上的情况），还有对很小的物体检测效果不好，这是因为一个网格中只预测了两个框，并且只属于一类。虽然每个格子可以预测B个bounding box，但是最终只选择只选择IOU最高的bounding box作为物体检测输出，即每个格子最多只预测出一个物体。当物体占画面比例较小，如图像中包含畜群或鸟群时，每个格子包含多个物体，但却只能检测出其中一个。这是YOLO方法的一个缺陷。 测试图像中，当同一类物体出现的不常见的长宽比和其他情况时泛化能力偏弱。 对不同大小的box预测中，相比于大box预测偏一点，小box预测偏一点肯定更不能被忍受的。而sum-square error loss中对同样的偏移loss是一样(为了缓和这个问题，作者用了一个比较取巧的办法，就是将box的width和height取平方根代替原本的height和width)。定位误差是影响检测效果的主要原因，尤其是大小物体的处理上，还有待加强。 YOLO loss函数中，大物体IOU误差和小物体IOU误差对网络训练中loss贡献值接近（虽然采用求平方根方式，但没有根本解决问题）。因此，对于小物体，小的IOU误差也会对网络优化过程造成很大的影响，从而降低了物体检测的定位准确性。 ","date":"2022-10-26","objectID":"/posts/fpn+ampamp+ssd+ampamp+yolo/:3:0","tags":null,"title":"FPN \u0026 SSD \u0026 YOLO","uri":"/posts/fpn+ampamp+ssd+ampamp+yolo/"},{"categories":null,"content":"Citation 深度学习中的FPN详解 什么是FPN(Feature Pyramid Networks–特征金字塔)？ SSD网络介绍 YOLO系列知识点整理 Thanks for reading! ","date":"2022-10-26","objectID":"/posts/fpn+ampamp+ssd+ampamp+yolo/:4:0","tags":null,"title":"FPN \u0026 SSD \u0026 YOLO","uri":"/posts/fpn+ampamp+ssd+ampamp+yolo/"},{"categories":null,"content":"ShuffleNet 简介 ShuffleNet是旷视科技提出的一种计算高效的CNN模型，其和MobileNet和SqueezeNet等一样主要是想应用在移动端。所以，ShuffleNet的设计目标也是如何利用有限的计算资源来达到最好的模型精度，这需要很好地在速度和精度之间做平衡。ShuffleNet是一种专门为计算资源有限的设备设计的神经网络结构，主要采用了pointwise group convolution和channel shuffle两种技术，在保留了模型精度的同时极大减少了计算开销。 旧网络存在问题 分组卷积GConv虽然能减少参数量和计算量，但是GConv中不同的组之间的信息没有交流。 在ResNeXt网络中，分组卷积占的计算量很少，大部分计算量都是1*1卷积产生的。 创新点 Channel Shuffle 先通过一个分组卷积，得到对应的特征矩阵，接下来使用channel shuffle操作后的特征矩阵进行组卷积，融合不同组之间的维度信息。 具体为，假设输入的feature map是一个一维的12个数据，分为3组，每一个分组有4个值，channel shuffle操作首先将这个矩阵进行升维，重构为一个g行n列的矩阵（这里g=3，n=4），然后对这个矩阵进行转置，后得到一个n行g列的新矩阵，再按照分组数g对这个得到的新矩阵进行展开，通过这一系列操作，达到了将原来固定的各分组打乱重排的目的。 ShuffleNet Unit ShuffleNet Unit的结构灵感来源于ResNet的中的bottleneck残差模块，从某种意义上来说，二者看起来几乎一样，以下是ShuffleNet Unit的结构： 相较于ResNet的bottleneck模块，ShuffleNet Unit有三处差别： ShuffleNet Unit将bottleneck模块中的3x3卷积替换为了3x3的depthwise卷积，也就是图中的DWConv，如图a所示。 对原残差模块的1x1卷积进行了替换，替换为了1x1的分组卷积，并在后面加上了一个channel shuffle操作，用于消除分组卷积的副作用，如图b所示。 在bottleneck的短路路径上使用了3x3的全局平均池化，然后将原来bottleneck模块的逐元素求和替换为了通道（Add）的拼接操作（Concat），如图c所示。使原来的数值1相加操作变成了维度的相加，更高的维度可以使网络拥有更多的通道数，从而提升网络的性能。 网络结构 基于shufflenet unit模块，整体的ShuffleNet网络结构图如下图所示： 上图为一个图像分类任务，输入为224x224的图像，输出1000个概率，同时作者将分组数分别设置为1到5，以此探索不同的分组数对网络性能的影响，其中一般以g=3作为网络的基准版本。 与传统的神经网络相比，ShuffleNet网络结构最大的区别在于拥有三个shufflenet unit模块，分别对应三个Stage，它们将输入特征图的尺寸减半，通道数加倍，除了升级版的shufflenet unit模块，网络的其他超参数与ResNet保持一致。 ","date":"2022-09-12","objectID":"/posts/shufflenet+ampamp+efficientnet/:1:0","tags":null,"title":"ShuffleNet \u0026 EfficientNet","uri":"/posts/shufflenet+ampamp+efficientnet/"},{"categories":null,"content":"Efficient Net 简介 EfficientNets是google在2019年5月发表的一个网络系列，使用神经架构搜索设计了一个baseline网络，并且将模型进行缩放获得一系列模型。它的精度和效率比之前所有的卷积网络都好。尤其是EfficientNet-B7在ImageNet上获得了当时最先进的 84.4%的top-1精度 和 97.1%的top-5精度，同时比之前最好的卷积网络大小缩小了8.4倍、速度提高了6.1倍。EfficientNets也可以很好的迁移，并且以更少的参量实现了最先进的精度——CIFAR-100（91.7%）、Flowers（98.8%）。 背景 通常为了获得更好的精度，放大卷积神经网络是一种广泛的方法。举个例子，ResNet可以通过使用更多层从ResNet-18放大到ResNet-200；目前为止，GPipe通过将baseline模型放大四倍在ImageNet数据集上获得了84.3%的top-1精度，然而，放大CNN的过程从来没有很好的理解过，目前通用的几种方法是放大CNN的深度、宽度和分辨率，在之前都是单独放大这三个维度中的一个，尽管任意放大两个或者三个维度也是可能的，但是任意缩放需要繁琐的人工调参同时可能产生的是一个次优的精度和效率。 模型缩放方法 Single Scaling（单尺度） 深度（d）：缩放网络深度在许多ConvNets都有使用，直觉上更深的网络可以捕获到更丰富和更复杂的特征，在新任务上也可以泛化的更好。然而，更深的网络由于梯度消失问题（这里我更倾向于说成是网络退化问题）也更难训练。尽管有一些技术，例如跨层连接、批量归一化等可以有效减缓训练问题，但是深层网络的精度回报减弱了：举个例子，ResNet-1000和ResNet-101具有类似的精度，即使它的层数更多。 宽度（w）：缩放网络宽度也是一种常用的手段，正如之前讨论过的，更宽的网络可以捕捉到更细粒度的特征从而易于训练。然而，非常宽而又很浅的网络在捕捉高层次特征时有困难。 分辨率（r）：使用更高分辨率的输入图像，ConvNets可能可以捕捉到更细粒度的模式。从最早的 224x224，现在有些ConvNets为了获得更高的精度选择使用 229x229 或者 331x331。目前，GPipe使用 480x480 的分辨率获得了最先进的ImageNet精度，更好的精度比如 600x600 也被广泛使用在目标检测网络中。 Compound Scaling（混合尺度） 论文中提出了Compound Scaling，同时对通过NAS得到的baseline模型Efficient-b0的深度（depth）、宽度（width）、输入图片分辨率（resolution）进行scaling，和 AutoML技术得到一系列网络模型EfficientNets。 为了了解网络缩放的效果，作者系统地研究了缩放不同维数对模型的影响。 虽然缩放单个维度可以提高模型性能，但作者观察到，根据可用资源平衡网络的所有维度ーー宽度、深度和图像分辨率ーー可以最大限度地提高整体性能。 复合缩放方法的第一步是执行网格搜索，以找到在固定资源约束下基线网络的不同缩放维度之间的关系。这决定了上面提到的每个维度的适当比例系数。 然后应用这些系数扩大基线网络，以达到期望的模型大小或资源要求。 与传统的缩放方法（上图a-d）相比，这种复合缩放方法（上图e）不断提高模型的精度和效率，可用于扩展现有的模型，如 mobileet (+ 1.4% 的图像集精度)和 ResNet (+ 0.7%)。 网络结构 作者指明，由于模型缩放不会改变基线网络中的层，但是拥有一个良好的基线网络也是至关重要的。作者使用现有的基础网络来评估该缩放方法，但是为了更好地证明该缩放方法的有效性，作者还开发了一种新的mobile-size baseline，称为 EfficientNet，EfficientNet-B0的网络结构如下 (类似于 MobileNetV2 和 MnasNet)： ","date":"2022-09-12","objectID":"/posts/shufflenet+ampamp+efficientnet/:2:0","tags":null,"title":"ShuffleNet \u0026 EfficientNet","uri":"/posts/shufflenet+ampamp+efficientnet/"},{"categories":null,"content":"Citation 【轻量化网络】ShuffleNet V1 论文研读 论文阅读笔记：ShuffleNet 令人拍案叫绝的EfficientNet和EfficientDet EfficientNet详解 Thanks for reading! ","date":"2022-09-12","objectID":"/posts/shufflenet+ampamp+efficientnet/:3:0","tags":null,"title":"ShuffleNet \u0026 EfficientNet","uri":"/posts/shufflenet+ampamp+efficientnet/"},{"categories":null,"content":"SENet 简介 SENet是2017ILSVRC2017（ImageNet Large Scale Visual Recognition Challenge）竞赛的冠军网络，在CVPR2018引用量第一。在深度学习领域，已经有很多成果通过在空间维度上对网络的性能进行了提升。而SENet反其道而行之，通过对通道关系进行建模来提升网络的性能。SENet较早的将注意力机制引入CNN中，使用了模块化设计。 亮点 引入通道注意力机制，关注channel之间的关系，希望模型可以自动学习到不同channel特征的重要程度。 网络结构 原理 SENet网络的创新点在于关注channel之间的关系，希望模型可以自动学习到不同channel特征的重要程度。为此，SENet提出了Squeeze-and-Excitation (SE)模块，SE模块首先对卷积得到的特征图进行Squeeze操作，得到channel级的全局特征，然后对全局特征进行Excitation操作，学习各个channel间的关系，也得到不同channel的权重，最后乘以原来的特征图得到最终特征。 本质上，SE模块是在channel维度上做attention或者gating操作，这种注意力机制让模型可以更加关注信息量最大的channel特征，而抑制那些不重要的channel特征。另外一点是SE模块是通用的，这意味着其可以嵌入到现有的网络架构中。 Squeeze 原始feature map的维度为H×W×C，其中H是高度（Height），W是宽度（width），C是通道数（channel）。Squeeze就是用全局平均池化（global average pooling）把H×W×C压缩为1×1×C。H×W压缩成一维后，相当于这一维参数获得了之前H×W全局的视野，感受区域更广。公式如下： Excitation Sequeeze操作得到了全局描述特征，接下来需要另外一种运算来提取channel之间的关系。 用两个全连接层来学习通道间的相关性，第一个FC层起到降维的作用，然后采用ReLU激活。最后的全连接层恢复原始的维度。公式如下： Reweight 将Excitation的输出权重作为经过特征选择后的每个特征通道的重要性，然后通过乘法逐通道加权到先前的特征上，完成在通道维度上的对原始特征的重标定。 全局平均池化 思想：对于输出的每一个通道的特征图的所有像素计算一个平均值，经过全局平均池化之后就得到一个 维度=类别数的特征向量，然后直接输入到softmax层。 作用：代替全连接层，可接受任意尺寸的图像。 优点： 可以更好的将类别与最后一个卷积层的特征图对应起来（每一个通道对应一种类别，这样每一张特征图都可以看成是该类别对应的类别置信图）； 降低参数量，全局平均池化层没有参数，可防止在该层过拟合； 整合了全局空间信息，对于输入图片的spatial translation更加鲁棒。 结构融合 上图是将SE模块嵌入到Inception结构的示例。方框旁边的维度代表该层的输出。这里使用Alobal Average Pooling作为Squeeze操作。紧接着两个Fully Connected层组成一个Bottleneck结构去建模通道间的相关性，并输出和输入特征同样数目的权重。首先将特征维度降低到输入的1/16，然后经过ReLu激活后再通过一个Fully Connected层升回到原来的维度。这样做相比直接用一个Fully Connected层的好处在于： 具有更多的非线性，可以更好地拟合通道间复杂的相关性； 减少了参数量和计算量。 下图是SE模块嵌入到ResNet的示例。操作过程基本和SE-Inception一样，只不过是在Addition前对分支上Residual的特征进行了特征重标定。如果对Addition后主支上的特征进行重标定，由于在主干上存在0~1的scale操作，在网络较深BP优化时就会在靠近输入层容易出现梯度消散的情况，导致模型难以优化。 运算效率 实验表明，SE模块在参数量上的增加带来的计算量增长微乎其微，但是性能却有所提升，当然这也取决于实际应用，如果因为SE模块导致参数量增加的掠夺，可以针对性的在适当的位置削减SE模块的数量，而精度几乎不受影响。 以ResNet-50和SE-ResNet-50对比举例来说，SE-ResNet-50相对于ResNet-50有着10%模型参数的增长。额外的模型参数都存在于Bottleneck设计的两个Fully Connected中，由于ResNet结构中最后一个stage的特征通道数目为2048，导致模型参数有着较大的增长，实验发现移除掉最后一个stage中3个build block上的SE设定，可以将10%参数量的增长减少到2%。此时模型的精度几乎无损失。 ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:1:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"MobileNet 简介 MobileNet V1是由google2016年提出，2017年发布的文章。其主要创新点在于深度可分离卷积，而整个网络实际上也是深度可分离模块的堆叠。它基于流线型架构，使用深度可分离卷积来构建轻量级深度神经网络，用于移动和嵌入式视觉应用。该网络引入了两个简单的全局超参数——宽度乘数和分辨率乘数，可以有效地在延迟和准确性之间进行权衡。 亮点 深度可分离卷积的使用 Global Average Pooling 的使用 用CONV/s2（步进2的卷积）代替MaxPool+CONV 深度可分离卷积的使用 在进行 depthwise 卷积时只使用了一种维度为in_channels的卷积核进行特征提取（没有进行特征组合）。采用 depth-wise convolution 会有一个问题，就是导致 信息流通不畅 ，即输出的 feature map 仅包含输入的 feature map 的一部分，在这里，MobileNet 采用了 point-wise(1*1) convolution 帮助信息在通道之间流通。 在进行 pointwise 卷积时只使用了output_channels 种 维度为in_channels 1*1 的卷积核进行特征组合，普通卷积不同 depth 层的权重是按照 1:1:1…:1的比例进行相加的，而在这里不同 depth 层的权重是按照不同比例(可学习的参数) 进行相加的。 参数数量由原来的p1 = F*F*in_channels*output_channels 变为了p2 = F*F*in_channels*1 + 1*1*in_channels*output_channels，减小为原来的p2/p1 = 1/output_channels + 1/F*F，其中 F 为卷积核的尺寸，若 F = 3，参数量大约会减少到原来的 1/8-1/9 。 原论文中对第一层没有用此卷积，深度可分离卷积中的每一个后面都跟 BN 和 RELU。其中，Relu6=min(max(0,x),6)。 ReLU6就是普通的ReLU但是限制最大输出值为6（对输出值做clip），这是为了在移动端设备float16的低精度的时候，也能有很好的数值分辨率，如果对ReLU的激活范围不加限制，输出范围为0到正无穷，如果激活值非常大，分布在一个很大的范围内，则低精度的float16无法很好地精确描述如此大范围的数值，带来精度损失。 两个超参数 Width Multiplier(α): Thinner Models 所有层的通道数（channel） 乘以 α 参数，模型大小近似下降到原来的 α^2 倍，计算量下降到原来的 α^2倍，α ∈ ( 0 , 1 ] with typical settings of 1, 0.75, 0.5 and 0.25，降低模型的宽度。 Resolution Multiplier(ρ \\rhoρ): Reduced Representation 输入层的 分辨率（resolution） 乘以 ρ 参数，等价于所有层的分辨率乘 ρ ，模型大小不变，计算量下降到原来的 ρ^2 倍，ρ ∈ ( 0 , 1 ]，降低输入图像的分辨率。 ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:2:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"MobileNet V2 简介 MobileNet V2基于倒置残差结构(inverted residual structure)，原本的残差结构的主分支是有三个卷积，两个逐点卷积通道数较多，而倒置的残差结构刚好相反，中间的卷积通道数（依旧使用深度分离卷积结构）较多，旁边的较小。此外，发现去除主分支中的非线性变换是有效的，这可以保持模型表现力。论文在多个数据集上做了对比实验，验证了该架构的有效性。 MobileNet V1缺点 结构问题 V1结构过于简单，没有复用图像特征，即没有concat/eltwise+ 等操作进行特征融合，而后续的一系列的ResNet，DenseNet等结构已经证明复用图像特征的有效性。 逐深度卷积问题： 在处理低维数据（比如逐深度的卷积）时，relu函数会造成信息的丢失。 DW 卷积由于本身的计算特性决定它自己没有改变通道数的能力，上一层给它多少通道，它就只能输出多少通道。所以如果上一层给的通道数本身很少的话，DW 也只能很委屈的在低维空间提特征，因此效果不够好。 主要改进点 引入倒残差（Inverted Residuals）结构，先升维再降维，增强梯度的传播，显著减少推理期间所需的内存占用。 去掉 Narrow layer（low dimension or depth） 后的 ReLU，保留特征多样性，增强网络的表达能力（Linear Bottlenecks）。 网络为全卷积的，使得模型可以适应不同尺寸的图像；使用 RELU6（最高输出为 6）激活函数，使得模型在低精度计算下具有更强的鲁棒性。 MobileNet V2 building block 如下所示，若需要下采样，可在 DWise 时采用步长为 2 的卷积；小网络使用小的扩张系数（expansion factor），大网络使用大一点的扩张系数（expansion factor），推荐是5~10，论文中 t = 6t。 具体改进 V2 去掉了第二个 PW 的激活函数改为线性激活。 论文作者称其为 Linear Bottleneck。原因如上所述是因为作者认为激活函数在高维空间能够有效的增加非线性，而在低维空间时则会破坏特征，不如线性的效果好。 V2 在 DW 卷积之前新加了一个 PW 卷积。 给每个 DW 之前都配备了一个 PW，专门用来升维，定义升维系数 t=6 ，这样不管输入通道数 Cin 是多是少，经过第一个 PW 升维之后，DW 都是在相对的更高维 ( t.Cin ) 进行更好的特征提取。 ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:3:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"MobileNet V3 简介 MobileNet V3是Google继MobileNetV2之后的又一力作，作为MobileNet系列的新成员，自然效果会提升，本文的一个亮点在于，网络的设计利用了NAS（network architecture search）算法以及NetAdapt algorithm算法。并且，本文还介绍了一些提升网络效果的trick，这些trick也提升了不少的精度以及速度。 亮点 互补搜索技术组合：由资源受限的NAS执行模块级搜索，NetAdapt执行局部搜索。 网络结构改进：将最后一步的平均池化层前移并移除最后一个卷积层，引入h-swish激活函数。 具体改进措施 引入SE结构 在bottlenet结构中加入了SE结构，并且放在了depthwise filter之后，如下图。因为SE结构会消耗一定的时间，所以作者在含有SE的结构中，将expansion layer的channel变为原来的1/4,这样作者发现，即提高了精度，同时还没有增加时间消耗。并且SE结构放在了depthwise之后。 修改尾部结构 在MobileNet V2中，avg pooling之前，存在一个1x1的卷积层，目的是提高特征图的维度，更有利于结构的预测，但是这其实带来了一定的计算量了，所以这里作者修改了，将其放在avg pooling的后面，首先利用avg pooling将特征图大小由7x7降到了1x1，降到1x1后，然后再利用1x1提高维度，这样就减少了7x7=49倍的计算量。并且为了进一步的降低计算量，作者直接去掉了前面纺锤型卷积的3x3以及1x1卷积，进一步减少了计算量，就变成了如下图第二行所示的结构，作者将其中的3x3以及1x1去掉后，精度并没有得到损失。这里降低了大约15ms的速度。 修改channel数量 修改头部卷积核channel数量，MobileNet V2中使用的是32 x 3 x 3，作者发现，其实32可以再降低一点，所以这里作者改成了16，在保证了精度的前提下，降低了3ms的速度。 非线性变换的改变 使用h-swish替换swish，swish是谷歌自家的研究成果，这次在其基础上，为速度进行了优化。swish与h-swish公式如下所示，由于sigmoid的计算耗时较长，特别是在移动端，这些耗时就会比较明显，所以作者使用ReLU6(x+3)/6来近似替代sigmoid，观察下图可以发现，其实相差不大的。利用ReLU有几点好处：1.可以在任何软硬件平台进行计算，2.量化的时候，它消除了潜在的精度损失，使用h-swish替换swith，在量化模式下回提高大约15%的效率，另外，h-swish在深层网络中更加明显。使用h-swish@16可以提高大约0.2%的精度，但是时间延长了大约20%。 设计方法 先使用NAS算法，优化每一个block，得到大体的网络结构，然后使用NetAdapt 算法来确定每个filter的channel的数量。 这里由于small model的精度以及耗时影响相对较大，mobilenet v3 large和mobilenet v3 small是分别使用NAS设计的。 NAS之后，可以使用NetAdapt算法设计每个layer，过程如下： 先用NAS找到一个可用的结构A。 在A的基础上生成一系类的候选结构，并且这些候选结构消耗在一点点减少，其实就是穷举子结构。 对于每个候选结构，使用前一个模型进行初始化，（前一个模型没有的参数随机初始化就行），finetune T个epoch，得到一个大致的精度。 在这些候选结构中，找到最好的。 反复迭代，知道目标时间到达，找到最合适的结果。 ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:4:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"Summary MobileNetV1 = VGG的标准卷积换成深度可分离卷积 MobileNetV2 = V1 + Inverted residuals + shortcut MobileNetV3 = V2 + SE-NET + h-swish ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:5:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"Citation SENet源码 SENet概览 论文解读《SENet》 一文带你深入全局平均池化 轻量级网络-Mobilenet系列(v1,v2,v3) MobileNetV1 \u0026 MobileNetV2 简介 轻量化网络——MobileNet mobilenet系列之又一新成员—mobilenet-v3 Thanks for reading! ","date":"2022-09-02","objectID":"/posts/senet+ampamp+mobilenet/:6:0","tags":null,"title":"SENet \u0026 MobileNet","uri":"/posts/senet+ampamp+mobilenet/"},{"categories":null,"content":"ResNet 简介 ResNet网络在 2015年由微软实验室中的何凯明等人提出，斩获当年ImageNet竞赛中分类任务第一名，目标检测第一名。获得COCO数据集中目标检测第一名，图像分割第一名。 亮点 超深的网络结构（超过1000层） 提出residual（残差结构）模块 使用Batch Normalization加速训练（丢弃Dropout） 网络结构 为什么采用residual 在ResNet提出之前，所有的神经网络都是通过卷积层和池化层的叠加组成的。人们认为卷积层和池化层的层数越多，获取到的图片特征信息越全，学习效果也就越好。但是在实际的试验中发现，随着卷积层和池化层的叠加，不但没有出现学习效果越来越好的情况，反而出现两种问题： 梯度消失和梯度爆炸。目前优化神经网络的方法都是基于BP，即根据损失函数计算的误差通过梯度反向传播的方式，指导深度网络权值的更新优化。其中将误差从末层往前传递的过程需要链式法则（Chain Rule）的帮助。而链式法则是一个连乘的形式，所以当层数越深的时候，梯度将以指数形式传播。梯度消失问题和梯度爆炸问题一般随着网络层数的增加会变得越来越明显。在根据损失函数计算的误差通过梯度反向传播的方式对深度网络权值进行更新时，得到的梯度值接近0或特别大，也就是梯度消失或爆炸。 退化问题。随着层数的增加，反传回来的梯度之间的相关性会越来越差，最后接近白噪声，预测效果反而越来越差。如下图所示 为了解决梯度消失或梯度爆炸问题，ResNet论文提出通过数据的预处理以及在网络中使用BN（Batch Normalization）层来解决。 为了解决深层网络中的退化问题，可以人为地让神经网络某些层跳过下一层神经元的连接，隔层相连，弱化每层之间的强联系。这种神经网络被称为残差网络 (ResNets)。ResNet论文提出了residual结构（残差结构）来减轻退化问题，下图是使用residual结构的卷积网络，可以看到随着网络的不断加深，效果并没有变差，而是变的更好了。 residual结构 residual结构使用了一种shortcut的连接方式，也可理解为捷径。让特征矩阵隔层相加，注意F(X)和X形状要相同，所谓相加是特征矩阵相同位置上的数字进行相加。若某一较深的网络多出另一较浅网络的若干层有能力学习到恒等映射，那么这一较深网络训练得到的模型性能一定不会弱于该浅层网络。 两种不同的residual： 对于深层次网络，使用左边的block意味着有很大的计算量，因此右侧使用1x1卷积先将输入进行降维，然后再经过3x3卷积后，最后用1x1卷积进行升维，为了主分支上输出的特征矩阵和捷径分支上输出的特征矩阵形状相同，以便进行加法操作。（搭建深层次网络时，采用右侧的残差结构） 降维时的shortcut 观察ResNet18层网络，可以发现有些残差块的shortcut是实线的，而有些则是虚线的。这些虚线的shortcut上通过1×1的卷积核进行了维度处理（特征矩阵在长宽方下采样，深度方向调整成下一层残差结构所需要的channel）。 Batch Normalization Batch Normalization是指批标准化处理，将一批数据的feature map满足均值为0，方差为1的分布规律。 对图像进行标准化处理，这样能够加速网络的收敛，如下图所示，对于Conv1来说输入的就是满足某一分布的特征矩阵，但对于Conv2而言输入的feature map就不一定满足某一分布规律了（注意这里所说满足某一分布规律并不是指某一个feature map的数据要满足分布规律，理论上是指整个训练样本集所对应feature map的数据要满足分布规律）。而我们Batch Normalization的目的就是使我们的feature map满足均值为0，方差为1的分布规律。计算公式如下： 假设feature1、feature2分别是由image1、image2经过一系列卷积池化后得到的特征矩阵，feature的channel为2，那么代表该batch的所有feature的channel1的数据，同理代表该batch的所有feature的channel2的数据。然后分别计算和的均值与方差，得到两个向量。然后在根据标准差计算公式分别计算每个channel的值（公式中的是一个很小的常量，防止分母为零的情况）。在我们训练网络的过程中，我们是通过一个batch一个batch的数据进行训练的，但是我们在预测过程中通常都是输入一张图片进行预测，此时batch size为1，如果在通过上述方法计算均值和方差就没有意义了。所以我们在训练过程中要去不断的计算每个batch的均值和方差，并使用移动平均(moving average)的方法记录统计的均值和方差，在训练完后我们可以近似认为所统计的均值和方差就等于整个训练集的均值和方差。然后在我们验证以及预测过程中，就使用统计得到的均值和方差进行标准化处理。如下图： ","date":"2022-08-26","objectID":"/posts/resnet+ampamp+densenet/:1:0","tags":null,"title":"ResNet \u0026 DenseNet","uri":"/posts/resnet+ampamp+densenet/"},{"categories":null,"content":"ResNet V2 简介 ResNet V2 的主要贡献在于通过理论分析和大量实验证明使用恒等映射（identity mapping）作为快捷连接（skip connection）对于残差块的重要性。同时，将BN/ReLu这些activation操作挪到了Conv（真正的weights filter操作）之前，提出预激活（Pre-activation）操作，并通过与后激活（Post-activation）操作做对比实验，表明对于多层网络，使用了预激活残差单元（Pre-activation residual unit）的resnet v2都取得了比 原版本resnet更好的结果。 亮点 提出了新的残差模块结构： 将激活函数移至旁路 full pre-activation 深度残差网络的分析 原先resnets的残差单元可表示为： 如果函数f也是恒等映射，则公式可以合并为： 那么任意深层的单元L与浅层单元l之间的关系可表示为： 该公式有以下两个特性： 深层单元的特征可以由浅层单元的特征和残差函数相加得到； 任意深层单元的特征都可以由起始特征x0与先前所有残差函数相加得到，这与普通（plain）网络不同，普通网络的深层特征是由一系列的矩阵向量相乘得到。残差网络是连加，普通网络是连乘。 该公式也带来了良好的反向传播特性，用ε表示损失函数，根据反向传播的链式传导规则，反向传播公式如下： 从上述公式中可以看出，反向传播也是两条路径，其中之一直接将信息回传，另一条会经过所有的带权重层。另外可以注意到第二项的值在一个mini-batch中不可能一直是-1，也就是说回传的梯度不会消失，不论网络中的权值的值再小都不会发生梯度消失现象。 恒等映射的重要性 考虑恒等映射的重要性。假设将恒等映射改为 $$ h(x_l) = \\lambda_l x_l $$ 则有： 递归调用得： 其中F表示将标量合并到残差函数中，反向传播公式如下： 与之前的公式不同，上公式的第一个加法项由因子 ∏i=lL−1λi 进行调节。对于一个极深的网络( L极大)，考虑第一个连乘的项，如果所有的 λ 都大于1，那么这一项会指数级增大；如果所有 λ 都小于1，那么这一项会很小甚至消失，会阻断来自shortcut的反向传播信号，并迫使其流过权重层。本文通过实验证明这种方式会对模型优化造成困难。另外其他不同形式的变换映射也都会阻碍信号的有效传播，进而影响训练进程。 不同形式激活函数的效果 BN after addition 效果比基准差，BN 层移到相加操作后面会阻碍信号传播，一个明显的现象就是训练初期误差下降缓慢。 ReLU before addition 这样组合的话残差函数分支的输出就一直保持非负，这会影响到模型的表示能力，而实验结果也表明这种组合比基准差。 Post-activation or pre-activation 原来的设计中相加操作后面还有一个ReLU激活函数，这个激活函数会影响到残差单元的两个分支，现在将它移到残差函数分支上，快捷连接分支不再受到影响。具体操作如下图： 根据激活函数与相加操作的位置关系，我们称之前的组合方式为“后激活（post-activation）”，现在新的组合方式称之为“预激活（pre-activation）”。预激活方式又可以分为两种：只将ReLU放在前面，或者将ReLU和BN都放到前面，实验结果显示full pre-activation的效果要更好。 使用预激活有两个方面的优点： f变为恒等映射，使得网络更易于优化； 使用BN作为预激活可以加强对模型的正则化。 Ease of optimization 这在训练1001层残差网络时尤为明显。使用原来设计的网络在起始阶段误差下降很慢，因为f是ReLU激活函数，当信号为负时会被截断，使模型无法很好地逼近期望函数；而使用预激活网络中的f是恒等映射，信号可以在不同单元直接直接传播。本文使用的1001层网络优化速度很快，并且得到了最低的误差。 f为ReLU对浅层残差网络的影响并不大。本文认为是当网络经过一段时间的训练之后权值经过适当的调整，使得单元输出基本都是非负，此时f不再对信号进行截断。但是截断现象在超过1000层的网络中经常发生。 Reducing overfitting 使用了预激活的网络的训练误差稍高，但却得到更低的测试误差，本文推测这是BN层的正则化效果所致。在原始残差单元中，尽管BN对信号进行了标准化，但是它很快就被合并到捷径连接(shortcut)上，组合的信号并不是被标准化的。这个非标准化的信号又被用作下一个权重层的输入。与之相反，本文的预激活（pre-activation）版本的模型中，权重层的输入总是标准化的。 ","date":"2022-08-26","objectID":"/posts/resnet+ampamp+densenet/:2:0","tags":null,"title":"ResNet \u0026 DenseNet","uri":"/posts/resnet+ampamp+densenet/"},{"categories":null,"content":"ResNeXt 简介 ResNeXt是ResNet和Inception的结合体，不同于Inception v4的是，ResNext不需要人工设计复杂的Inception结构细节，而是每一个分支都采用相同的拓扑结构。ResNeXt的本质是分组卷积（Group Convolution），通过变量基数（Cardinality）来控制组的数量。分组卷机是普通卷积和深度可分离卷积的一个折中方案，即每个分支产生的Feature Map的通道数为 n(n\u003e1) 。 神经网络标准范式 split-transform-merge 如上图所示，先将输入分配到多路，然后每一路进行转换，最后再把所有支路的结果融合。 基本结构 具体结构 类似ResNet，作者选择了很简单的基本结构，每一组C个不同的分支都进行相同的简单变换，上面是ResNeXt-50（32x4d）的配置清单，32指进入网络的第一个ResNeXt基本结构的分组数量C（即基数）为32，4d表示depth即每一个分组的通道数为4（所以第一个基本结构输入通道数为128）。 可以看到ResNet-50和ResNeXt-50（32x4d）拥有相同的参数，但是精度却更高。具体实现上，因为1x1卷积可以合并，就合并了，代码更简单，并且效率更高。 得益于精心设计的复杂的网络结构，ResNet-Inception v2可能效果会更好一点，但是ResNeXt的网络结构更简单，可以防止对于特定数据集的过拟合。而且更简单的网络意味着在用于自己的任务的时候，自定义和修改起来更简单。 总结 ResNeXt提出了一种介于普通卷积核深度可分离卷积的这种策略：分组卷积，他通过控制分组的数量（基数）来达到两种策略的平衡。分组卷积的思想是源自Inception，不同于Inception的需要人工设计每个分支，ResNeXt的每个分支的拓扑结构是相同的。最后再结合残差网络，得到的便是最终的ResNeXt。 从上面的分析中我们可以看书ResNeXt的结构非常简单，但是其在ImageNet上取得了由于相同框架的残差网络，也算是Inception直接助攻了一把吧。 ResNeXt确实比Inception V4的超参数更少，但是他直接废除了Inception的囊括不同感受野的特性仿佛不是很合理，在更多的环境中我们发现Inception V4的效果是优于ResNeXt的。类似结构的ResNeXt的运行速度应该是优于Inception V4的，因为ResNeXt的相同拓扑结构的分支的设计是更符合GPU的硬件设计原则。 ","date":"2022-08-26","objectID":"/posts/resnet+ampamp+densenet/:3:0","tags":null,"title":"ResNet \u0026 DenseNet","uri":"/posts/resnet+ampamp+densenet/"},{"categories":null,"content":"DenseNet 简介 DenseNet模型的基本思路与ResNet一致，但是它建立的是前面所有层与后面层的密集连接。DenseNet脱离了加深网络层数(ResNet)和加宽网络结构(Inception)来提升网络性能的定式思维，从特征的角度考虑，通过特征重用和旁路(Bypass)设置，既大幅度减少了网络的参数量，又在一定程度上缓解了gradient vanishing问题的产生。这些特点让DenseNet在参数和计算成本更少的情形下实现比ResNet更优的性能，DenseNet也因此斩获CVPR 2017的最佳论文奖。 亮点 由于密集连接方式，DenseNet提升了梯度的反向传播，使得网络更容易训练。由于每层可以直达最后的误差信号，实现了隐式的“deep supervision”。 参数更小且计算更高效，这有点违反直觉，由于DenseNet是通过concat特征来实现短路连接，实现了特征重用，并且采用较小的growth rate，每个层所独有的特征图是比较小的。 由于特征复用，最后的分类器使用了低级特征。 设计理念 相比ResNet，DenseNet提出了一个更激进的密集连接机制：即互相连接所有的层，具体来说就是每个层都会接受其前面所有层作为其额外的输入。下图1为ResNet网络的连接机制，作为对比，下图2为DenseNet的密集连接机制。可以看到，ResNet是每个层与前面的某层（一般是2~3层）短路连接在一起，连接方式是通过元素级相加。而在DenseNet中，每个层都会与前面所有层在channel维度上连接（concat）在一起（这里各个层的特征图大小是相同的），并作为下一层的输入。对于一个L层的网络，DenseNet共包含L(L+1)/2个连接，相比ResNet，这是一种密集连接。而DenseNet是直接concat来自不同层的特征图，这可以实现特征重用，提升效率，这一特点是DenseNet与ResNet最主要的区别。 网络结构 假设输入为一个图片 X0 , 经过一个L层的神经网络，其中第i层的非线性变换记为 Hi ()，Hi ()可以是多种函数操作的累加如BN、ReLU、Pooling或Conv等。第i层的特征输出记作 Xi。 传统卷积前馈神经网络将第i层的输出Xi作为i+1层的输入，可以写作Xi = Hi ( Xi−1 )。ResNet增加了旁路连接，可以写作Xl = Xl ( Xl−1 )+ Xl−1。 ResNet的一个最主要的优势便是梯度可以流经恒等函数来到达靠前的层，但恒等映射和非线性变换输出的叠加方式是相加，这在一定程度上破坏了网络中的信息流。 为了进一步优化信息流的传播，DenseNet提出了上图的网络结构。如图所示，第i层的输入不仅与i-1层的输出相关，还有所有之前层的输出有关，记作: Xl = Hl ([ X0 , X1 ,…, Xl−1 ])， 其中[]代表concatenation(拼接)，既将 X0 到 Xl−1 层的所有输出feature map按Channel组合在一起。这里所用到的非线性变换H为BN+ReLU+ Conv(3×3)的组合。 DenseBlock+Transition CNN网络一般要经过Pooling或者stride\u003e1的Conv来降低特征图的大小，而DenseNet的密集连接方式需要特征图大小保持一致。为了解决这个问题，DenseNet网络中使用DenseBlock+Transition的结构。 DenseBlock是包含很多层的模块，每个层的特征图大小相同，层与层之间采用密集连接方式。 Transition模块是连接两个相邻的DenseBlock，并且通过Pooling使特征图大小降低。 总结 一些较早层提取出的特征仍可能被较深层直接使用。 即使是Transition layer也会使用到之前Denseblock中所有层的特征。 第2-3个Denseblock中的层对之前Transition layer利用率很低,说明transition layer输出大量冗余特征.这也为DenseNet-BC提供了证据支持,既Compression的必要性。 最后的分类层虽然使用了之前Denseblock中的多层信息,但更偏向于使用最后几个feature map的特征,说明在网络的最后几层,某些high-level的特征可能被产生。 ","date":"2022-08-26","objectID":"/posts/resnet+ampamp+densenet/:4:0","tags":null,"title":"ResNet \u0026 DenseNet","uri":"/posts/resnet+ampamp+densenet/"},{"categories":null,"content":"Citation ResNet详解 你必须要知道CNN模型：ResNet Backbone论文-ResNet v2 解读 深度学习——分类之ResNeXt ResNeXt详解 详解DenseNet(密集连接的卷积网络) DenseNet详解 Thanks for reading! ","date":"2022-08-26","objectID":"/posts/resnet+ampamp+densenet/:5:0","tags":null,"title":"ResNet \u0026 DenseNet","uri":"/posts/resnet+ampamp+densenet/"},{"categories":null,"content":"AlexNet 简介 AlexNet是2012年ISLVRC 2012（ImageNet Large Scale Visual Recognition Challenge）竞赛的冠军网络，分类准确率由传统的 70%+提升到 80%+。由Hinton和他的学生Alex Krizhevsky设计。也是在那年之后，深度学习开始迅速发展。 亮点 首次使用GPU进行模型训练。 使用了ReLU激活函数，而不是传统的Sigmoid激活函数以及Tanh激活函数。主要原因是ReLU函数在进行梯度下降的计算过程中能显著加快训练过程，也就是非饱和的线性的激活函数要快于sigmoid和tanh等饱和的非线性激活函数的收敛速度（在这篇论文中并没有谈及ReLU函数对于梯度消失问题的解决，只是从收敛速度上论述的）。 使用了LRN局部响应归一化（Local Response Normalization，LRN）。AlexNet中提出局部响应归一化层（LRN）,是跟在激活或池化之后的一种为了提高准确度的技术方法。LRN的主要思想就是对局部神经元的神经活动创建竞争机制，使得其中响应比较大的值变得相对更大，并抑制其他反馈更小的神经元，从而也在一定程度上增强了泛化能力。 在全连接层的前两层中使用了Dropout随机失活神经元操作，以减少过拟合。 模型架构 ReLU激活函数 针对sigmoid梯度饱和导致训练收敛慢的问题，在AlexNet中引入了ReLU。ReLU是一个分段线性函数，小于等于0则输出为0；大于0的则恒等输出。相比于sigmoid，ReLU有以下有特点： 计算开销小。sigmoid的正向传播有指数运算，倒数运算，而ReLu是线性输出；反向传播中，sigmoid有指数运算，而ReLU有输出的部分，导数始终为1。 梯度饱和问题。 稀疏性。ReLU会使一部跟神经元的输出为0，这样造就了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 Dropout 过拟合的根本原因是特征维度过多，模型过于复杂，参数过多，训练数据过少，噪声过多，导致模型完美拟合训练集，但缺乏泛化能力，对新数据的测试集预测结果差。 Dropout的使用有效地缓解了模型复杂度提升导致的过拟合。Dropout通过修改神经网络本身结构来实现，对于某一层的神经元，通过定义的概率将神经元置为0，这个神经元就不参与前向和后向传播，就如同在网络中被删除了一样，同时保持输入层与输出层神经元的个数不变，然后按原有的学习方法进行参数更新。在下一次迭代中，又重新随机删除一些神经元（置为0），直至训练结束。 Dropout应该算是AlexNet中一个很大的创新，现在神经网络中的必备结构之一。Dropout也可以看成是一种模型组合，每次生成的网络结构都不一样，通过组合多个模型的方式能够有效地减少过拟合，Dropout只需要两倍的训练时间即可实现模型组合（类似取平均）的效果，非常高效。 ","date":"2022-08-20","objectID":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/:1:0","tags":null,"title":"AlexNet \u0026 VGG \u0026 GoogLeNet","uri":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/"},{"categories":null,"content":"VGG 简介 VGG是由牛津大学视觉几何小组（Visual Geometry Group, VGG）提出的一种深层卷积网络结构，赢得了 2014 年 ILSVRC 分类任务的亚军（冠军由 GoogLeNet 夺得）和定位任务的冠军。VGG可以看成是加深版本的AlexNet，都是conv layer + FC layer。该网络是在ILSVRC 2014上的相关工作，主要工作是证明了增加网络的深度能够在一定程度上影响网络最终的性能。 亮点 证明了增加网络的深度能够在一定程度上影响网络最终的性能。 小卷积核和小池化核，卷积核全部替换为3*3（极少用了1*1），相比AlexNet的3*3池化核，VGG全部为2*2的池化核。 层数更深特征图更宽，由于卷积核专注于扩大通道数、池化专注于缩小宽和高，使得模型架构上更深更宽的同时，计算量的增加放缓。 全连接层换成卷积层，网络测试阶段将训练阶段的三个全连接替换为三个卷积，测试重用训练时的参数，使得测试得到的全卷积网络因为没有全连接的限制，因而可以接收任意宽或高为的输入。 模型架构 网络特点 两个3x3的卷积堆叠获得的感受野大小，相当一个5x5的卷积；而3个3x3卷积的堆叠获取到的感受野相当于一个7x7的卷积。好处如下： 多层卷积引入了多次非线性变换，可以增加网络的非线性表达能力。 两层3*3卷积核的参数量比一层5*5卷积核的参数量更少，可以看作对网络做了相应的正则化。 网络测试阶段将训练阶段的三个全连接替换为三个卷积，使得测试阶段对输入图片尺寸没有限制。使用全连接层和卷积层的区别： ​ 全连接层：通道数相同的情况下，特征图大小一样，才能保证与全连接层中的神经元数目相同。 ​ 卷积层：通道数目来决定最终的分类输出数目，不需要特征图大小相同。 ","date":"2022-08-20","objectID":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/:2:0","tags":null,"title":"AlexNet \u0026 VGG \u0026 GoogLeNet","uri":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/"},{"categories":null,"content":"GoogLeNet 简介 GoogLeNet是2014年Christian Szegedy提出的一种全新的深度学习结构，在这之前的AlexNet、VGG等结构都是通过增大网络的深度来获得更好的训练效果，但层数的增加会带来很多负作用，比如过拟合、梯度消失、梯度爆炸等。inception的提出则从另一种角度来提升训练结果：更高效的利用计算资源，在相同的计算量下能提取到更多的特征，从而提升训练结果。 前代网络缺陷 ​ LeNet，AlexNet，VGG这些串联网络通过增大网络的深度来获得更好的训练效果，主要存在以下缺陷： 提取的特征图尺度单一。串联网络中，每一层级的卷积核都是固定尺寸的，只能提取固定尺度的特征。基于这种尺度单一的特征图构建的模型鲁棒性不强，泛化能力差。 参数量巨大，且难以将梯度传递到网络顶层。虽然多层小卷积核堆叠取得大卷积核能够减少参数量，但杯水车薪，网络越深也越容易发生梯度消失，使得网络难以训练。 解决方案及亮点 使用1x1的卷积来进行升降维。1×1卷积核的重要作用：降维或升维、跨通道信息交融、减少参数量、增加模型深度并提高非线性表达能力。 与直觉相一致，即视觉信息应该在不同的尺度上进行处理再聚合。 引入稀疏特性、将全连接层换成稀疏连接，利用稀疏矩阵分解成密集矩阵计算的原理来加快收敛速度： 空间（spatial）维度，只对图像的某一部分进行卷积，而不是对整个图像进行卷积。 特征（feature）维度，在多个尺寸上进行卷积再聚合，把相关性强的特征聚集到一起，每一种尺寸的卷积只输出256个特征中的一部分。 各版本inception改进 inception-v1 将多尺度的卷积层、池化层提取的特征图拼接输入下一层，提升模型多尺度特征提取能力； 利用1*1卷积对输入通道降维，减少模型参数量，并引入更多的非线性，提升模型泛化能力。 inception-v2 将Inception-v1中的大尺寸卷积核替换为多个小尺寸卷积核的堆叠，减少模型参数量，增加网络深度，提升模型拟合复杂分布的能力； 如果在训练模型时，每一批次的训练样本分布差别较大，那么网络的每一层都要重新去适应学习新的数据分布，这非常影响网络的训练速度，因此Inception-v2对卷积层进行了BatchNormalizatioin，有效地加速了网络训练过程，也能够消除梯度弥散； 引入了辅助分类器，即在靠近输出层的层级上引入旁路分类器，来解决梯度无法反向传递非常深的问题，辅助分类器只在训练时使能，测试时不开启辅助分类器； 结合了对称卷积与非对称卷积，增加了网络宽度。 inception-v3 inception-v3的改进基于以下几点优化思路： 避免特征表示瓶颈。尤其在网络前端，要避免严重压缩导致的瓶颈：特征表示尺寸应该从输入到输出端温和地减少，避免特征信息大量丢失。 高维信息更适合在网络的局部处理。在卷积网络中逐步增加非线性激活函数响应可以解耦更多的特征，加速网络训练。 空间聚合可以通过低维嵌入，不会导致网络表示能力的下降。在进行大尺寸卷积之前，在空间聚合前先对输入信息进行降维处理，可以加速训练。 平衡好网路的深度和宽度。通过平衡网络每层滤波器的个数和网络的层数可以使网络达到最佳性能。并行增加网络的深度和宽度时，模型性能提升最大。 改进点如下： 优化算法使用RMSProp替代SGD。 使用Label Smoothing Regularization方法。LSR是一种通过在输出y中加入噪声，对模型进行约束，降低模型过拟合的方法。 将大尺寸卷积核替换成小尺寸卷积核。 对辅助分类器中的全连接层进行BatchNormalization。 inception-v4 Inception-v4基本沿袭了Inception-v2/v3的设计，它的各个模块在结构上更加统一。 inception-ResNet Inception-ResNet借鉴了何凯明的残差连接思想，将Inception模块与残差连接进行结合。残差连接是指浅层特征通过另外一条支路直接加到高层特征中，达到特征复用的目的，可以有效避免深层网络的梯度弥散问题。 ","date":"2022-08-20","objectID":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/:3:0","tags":null,"title":"AlexNet \u0026 VGG \u0026 GoogLeNet","uri":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/"},{"categories":null,"content":"Self-thinking 提高深度神经网络性能方法 增加网络大小，包括深度和宽度，这是最直接的办法，也是最简单最安全的办法，尤其是当给定大量带标签的训练数据时。但是缺点明显：增加网络中训练的参数（导致过拟合），增加计算量。 上述问题的解决方案 从全连接层或卷积层完全转变到稀疏连接的结构。Arora等人的开创性工作表明如果数据集的概率分布可以用一个大的、非常稀疏的深度神经网络来表示，那么可以通过分析最后一层激活函数的相关统计量（均值方差等）和聚合有着高度相关输出的神经元来逐层生成最优的网络拓扑，这与赫布原理相仿——神经元一起发射且连接在一起。 Inception系列引发的思考（好的深度网络设计原则） 逐层构造网络。如果数据集的概率分布能够被一个神经网络所表达，那么构造这个网络的最佳方法是逐层构筑网络，即将上一层高度相关的节点连接在一起。几乎所有效果好的深度网络都具有这一点，不管AlexNet，VGG堆叠多个卷积，googLeNet堆叠多个inception模块，还是ResNet堆叠多个resblock。 稀疏的结构。人脑的神经元连接就是稀疏的，因此大型神经网络的合理连接方式也应该是稀疏的。稀疏的结构对于大型神经网络至关重要，可以减轻计算量并减少过拟合。 卷积操作（局部连接，权值共享）本身就是一种稀疏的结构，相比于全连接网络结构是很稀疏的。 符合Hebbian原理。Cells that fire together, wire together. 一起发射的神经元会连在一起。 相关性高的节点应该被连接而在一起。 而inception中 1×1的卷积恰好可以融合以上三者。一层可能会有多个卷积核，在同一个位置但在不同通道的卷积核输出结果相关性极高。一个1×1的卷积核可以很自然的把这些相关性很高，在同一个空间位置，但不同通道的特征结合起来。而其它尺寸的卷积核（3×3，5×5）可以保证特征的多样性，因此也可以适量使用。 总结 运用赫布原理和多尺度并行处理：从FC层转变到稀疏结构。 Inception模块的集成：使用1×1卷积核进行降维再用5×5卷积核升维。一个googlenet里包含九个Inceptionv1模块。 全局平均池化替代FC：减少参数量。做迁移学习更方便，用在弱监督学习和半监督学习中非常的可靠。 ","date":"2022-08-20","objectID":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/:4:0","tags":null,"title":"AlexNet \u0026 VGG \u0026 GoogLeNet","uri":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/"},{"categories":null,"content":"Citation AlexNet论文阅读总结及代码 VGG网络详解 一文读懂VGG网络 GoogLeNet论文翻译及解读 深度学习-GoogLeNet Thanks for reading! ","date":"2022-08-20","objectID":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/:5:0","tags":null,"title":"AlexNet \u0026 VGG \u0026 GoogLeNet","uri":"/posts/alexnet+ampamp+vgg+ampamp+googlenet/"},{"categories":null,"content":"经典篇 Visual Perception (视觉感知) Characterizing edges (特征边缘)：边缘是指图像强度函数中急剧变化的位置。 图像滤波：在尽量保留图像细节特征的条件下对目标图像的噪声进行抑制，是图像处理中不可缺少的操作，其处理效果的好坏将直接影响到后续图像处理和分析的有效性和可靠性。 canny边缘检测算法： 用高斯滤波器对图像进行平滑处理； 利用一阶偏导算子找到图像灰度沿水平方向和垂直方向的偏导数，并求出梯度的幅值和方位； 非极大值抑制，找到局部梯度最大值； 用双阈值算法检测和链接边缘，凡大于高阈值T1的一定是边缘，凡小于低阈值T2的一定不是边缘，若既大于低阈值又小于高阈值，则要看这个像素的邻接像素中是否有大于高阈值的边缘像素，若有则是边缘，否则不是。 Feature Extraction (特征提取) Bag of Words, BoW (词袋模型) ","date":"2021-08-13","objectID":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/:1:0","tags":null,"title":"新一代视觉感知综述","uri":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/"},{"categories":null,"content":"现代篇 CNN (卷积神经网络) Vision Transformer (视觉自注意力网络) ","date":"2021-08-13","objectID":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/:2:0","tags":null,"title":"新一代视觉感知综述","uri":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/"},{"categories":null,"content":"前沿篇 XAI (可解释性AI) Contrastive Pretraining (对比预训练） Multimodal Fusion (多模态融合） ","date":"2021-08-13","objectID":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/:3:0","tags":null,"title":"新一代视觉感知综述","uri":"/posts/%E6%96%B0%E4%B8%80%E4%BB%A3%E8%A7%86%E8%A7%89%E6%84%9F%E7%9F%A5%E7%BB%BC%E8%BF%B0/"},{"categories":null,"content":"RetinaNet 简介 RetinaNet 是 Tsung-Yi Lin 和 Kaiming He（四作） 于 2018 年发表的论文 Focal Loss for Dense Object Detection。深入分析了极度不平衡的正负（前景背景）样本比例导致 one-stage 检测器精度低于 two-stage 检测器，基于上述分析，提出了一种简单但是非常实用的 Focal Loss 焦点损失函数，并且 Loss 设计思想可以推广到其他领域，同时针对目标检测领域特定问题，设计了 RetinaNet 网络，结合 Focal Loss 使得 one-stage 检测器在精度上能够达到乃至超过 two-stage 检测器。 网络结构 RetinaNet的特征提取网络选择了残差网络ResNet，特征融合这块选择了FPN（特征金字塔网络），以特征金字塔不同的尺寸特征图作为输入，搭建三个用于分类和框回归的子网络。分类网络输出的特征图尺寸为（W,H,KA)，其中W、H为特征图宽高，KA为特征图通道，存放A个anchor各自的类别信息（K为类别数）。 历史问题 在One stage中，detector直接在类别不平衡（负样本很多，正样本很少）中进行分类和回归，直接输出bbox和类别，原有的交叉熵损失无法处理这种不平衡，导致训练不充分，精度低，但是却提升了检测速度。 在Two stage中，FPN网络已经过滤了一部分的背景bbox，因此在fast r-cnn中正负样本比例较均衡，因此准确率较高。 针对所有的负样本，数量过多，主导了损失函数，不利于模型收敛。 针对单个负样本，大多数负样本不包含任何物体，属于易分样本，且易分样本数量很多，训练时对应背景类的预测得分会很高，那么单个样本的loss就很小，反向计算时梯度小，造成易分负样本对loss的收敛作用有限。 主要贡献 提出Focal Loss：解决one-stage算法中，样本不平衡和难易样本的问题。 样本不平衡：保证在损失函数中，正样本与负样本的贡献（比重）均衡。 难易样本：希望模型更关注难分样本，难分样本在loss中的比重更大。 Focal Loss 二分类交叉熵损失函数： 解决正负样本不均：加上一个权重α（范围[0,1]） 解决难易样本不均：引入因子γ(≥0)，将高置信度的样本的损失权重降低 Focal Loss 展开后为 优缺点分析 优点：提升准确率，降低了正负样本和难易样本不均衡带来的影响； 缺点：Focal Loss易受噪声干扰，对图像标注的准确性要求非常高，一旦有标错的样本，就会被focal loss当做困难样本，从而影响学习效果。 ","date":"2021-05-27","objectID":"/posts/retinanet+ampamp+fcos/:1:0","tags":null,"title":"RetinaNet \u0026 FCOS","uri":"/posts/retinanet+ampamp+fcos/"},{"categories":null,"content":"FCOS 简介 FCOS 发表于ICCV 2019，是一阶段anchor free目标检测算法，其主要的卖点为无锚。通过回归特征图上每个位置距离目标框的上下左右距离来实现目标检测。如果一个位置落在了多个目标框内，文中的方法是通过多尺度+回归幅度限制的方法来缓解这个问题。为了解决目标框数量过多的问题，文中提出了center-ness的方法，为每个位置学习一个center-ness分数，最后乘以预测类别分数作为非极大抑制的输入参数来解决这个问题。 核心思想 将铺设锚框变为铺设锚点，进行物体检测。 所谓铺设锚框，又称为Anchor-Based，是指在输出特征图上的每个像素的位置，放置几个预先定义的anchor框，在网络训练过程中，对这些anchor框进行分类与回归。 通过GT框和这些anchor框计算IoU，依据设定的阈值条件来定义正负样本，典型的有YOLOv3。 所谓铺设锚点，又称为Anchor-free，如下图所示，将原有的 对锚框进行分类与回归，变为对锚点进行分类与回归，其中回归是预测锚点到检测（GT）框上下左右四条边界的距离，典型的有FCOS。 Anchor-Based缺点 Anchor-Based方式，检测性能对于anchor的大小、数量、长宽比 都非常敏感，通过改变这些超参数Retinanet在COCO benchmark上面提升了4%的MAP。 固定size和aspect ratio的anchor损害了检测器的普适性，导致对于不同任务，anchor可能需要重新设置大小和长宽比。 为了达到更高的召回率（查全率），需要生成大量的anchor（FPN约18万个），但是大部分的anchor在训练时标记为负样本（negative），造成了样本不均衡问题。 在训练中，需要计算所有anchor与真实框的IoU，这样会消耗大量内存和计算资源。 FCOS优点 检测问题被统一到 FCN-solvable 的问题，可以简单地重用其他任务的idea，如语义分割。 anchor-free方式，不需要像anchor-based那样大量调整参数，使训练更为简单。 由于不需要计算IoU，节省了大量算力和内存。 提出了一些关于交叠区域的解决方法和思考。 模型部署会受到两种限制，一种是计算量的限制，一种是I/O 带宽的限制。anchor-free方式相比于anchor-based方式，对部署更友好一些。 网络结构 FCOS采用FPN结构，backbone的C3、C4、C5特征层作为FPN的输入，FPN生成P3、P4、P5、P6、P7特征图，送入后续的检测头Head。 每个Head包含3个分支： classification分支：预测类别，图中的C表示类别数，相当于C个二分类； regression分支：回归位置，图中的4表示：l、t、r、b，预测锚点到检测框上下左右四条边界的距离； center-ness：中心度，一个锚点对应一个中心度，用于锚点相对于检测框中心性的判断 在检测子网络Head中，分类分支和回归分支都先经过了4个卷积层进行了特征强化。 早期版本，在分类分支中，既包含 正、负样本锚点的 类别预测分支，又包含正、负样本锚点中心性判断的center-ness分支，用来强化检测结果； 回归分支用来回归正样本锚点到检测框上、下、左、右四个边界的距离 。 损失函数 ","date":"2021-05-27","objectID":"/posts/retinanet+ampamp+fcos/:2:0","tags":null,"title":"RetinaNet \u0026 FCOS","uri":"/posts/retinanet+ampamp+fcos/"},{"categories":null,"content":"Citation RetinaNet 一阶段目标检测器-RetinaNet网络详解 目标检测FCOS的初步理解 FCOS理论知识讲解 Thanks for reading! ","date":"2021-05-27","objectID":"/posts/retinanet+ampamp+fcos/:3:0","tags":null,"title":"RetinaNet \u0026 FCOS","uri":"/posts/retinanet+ampamp+fcos/"},{"categories":null,"content":"RCNN 简介 R-CNN的全称是Region-CNN，是第一个成功将深度学习应用到目标检测上的算法。R-CNN基于卷积神经网络，线性回归，和支持向量机等算法，实现目标检测技术。R-CNN遵循传统目标检测的思路，同样采用提取框，对每个框提取特征、图像分类、 非极大值抑制四个步骤进行目标检测。只不过在提取特征这一步，将传统的特征（如 SIFT、HOG 特征等）换成了深度卷积网络提取的特征。 算法流程 候选区域生成 使用Selective Search算法对每张输入图像使用选择性搜索来选取多个高质量的候选区域（Region Proposal）。这个算法先对图像基于像素信息做快速分割来得到多个区域，然后将当下最相似的两区域合并成一个区域，重复进行合并直到整张图像变成一个区域。最后根据合并的信息生成多个有层次结构的提议区 域，并为每个提议区域生成物体类别和真实边界框。 特征提取 选取一个预先训练好的卷积神经网络，去掉最后的输出层来作为特征抽取模块。对每个提议区域，将其变形成卷积神经网络需要的输入尺寸后进行前向计算抽取特征。 SVM分类器 将每个提议区域的特征连同其标注做成一个样本，训练多个支持向量机(SVM)来进行物 体类别分类，这里第 i 个 SVM 预测样本是否属于第 i 类。 边界框回归器 在这些样本上训练一个线性回归模型来预测（精修）真实边界框。对于SVM分好类的候选区域做边框回归，用Bounding box回归值校正原来的建议窗口，生成预测窗口坐标 Selective Search Selective Search（选择性搜索）是用于目标检测的region proposal算法，它计算速度快，具有很高的召回率，基于颜色，纹理，大小和形状兼容计算相似区域的分层分组。 图像中区域特征比像素更具代表性，作者使用Felzenszwalb and Huttenlocher的方法产生图像初始区域，使用贪心算法对区域进行迭代分组： 计算所有邻近区域之间的相似性； 两个最相似的区域被组合在一起； 计算合并区域和相邻区域的相似度； 重复2、3过程，直到整个图像变为一个地区。 在每次迭代中，形成更大的区域并将其添加到区域提议列表中。以自下而上的方式创建从较小的细分segments到较大细分segments的region proposal。 Bounding box regression Rcnn的Bounding box regression（边框回归）是指对粗略的预测框P进行平移和尺度放缩来微调， 使得经过微调后的窗口G’跟Ground Truth G更接近， 这样定位会更准确，其详细数学原理参考边框回归(Bounding Box Regression)详解。 ","date":"2020-09-21","objectID":"/posts/rcnn+ampamp+fast-rcnn+ampamp+faster-rcnn/:1:0","tags":null,"title":"Rcnn \u0026 Fast-Rcnn \u0026 Faster-Rcnn","uri":"/posts/rcnn+ampamp+fast-rcnn+ampamp+faster-rcnn/"},{"categories":null,"content":"Citation 边框回归(Bounding Box Regression)详解 理解Selective Search Thanks for reading! 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    @@ -38,19 +38,68 @@ href="/posts/" - > 文章
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    @@ -38,19 +38,68 @@ href="/posts/" - > 文章
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    • @@ -144,5 +248,5 @@
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    + diff --git a/categories/index.html b/categories/index.html index 9160c01..c547b0d 100644 --- a/categories/index.html +++ b/categories/index.html @@ -19,7 +19,7 @@ - +
    @@ -38,19 +38,68 @@ href="/posts/" - > 文章
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    • @@ -194,5 +298,5 @@

    -
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    @@ -38,19 +38,68 @@ href="/posts/" - > 文章
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    • @@ -144,5 +248,5 @@
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    + diff --git a/categories/tast_cate/index.html b/categories/tast_cate/index.html index 3350d1e..16ec5ec 100644 --- a/categories/tast_cate/index.html +++ b/categories/tast_cate/index.html @@ -19,7 +19,7 @@ - +
    @@ -38,19 +38,68 @@ href="/posts/" - > 文章
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    @@ -38,19 +38,68 @@ href="/posts/" - > 文章
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