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From AlexNet to SqueezeNet

By 陳柏翔 (Shawn Chen)

Introduction

This is a study report about convolution network architectures. After studying cs231n, one should have a fairly good idea about how convnet operates and might also remember those famous ConvNets' names mentioned in class (e.g. AlexNet, VGG, GoogLeNet, ResNet). However, to me, it's not clear why those networks are constructed in such a way, and I was eagerly wanting to know how people explore and create new convnet architectures. Therefore, after reading through the original papers, I tried to come up with my own way of thinkings that how convnets are developed and evolved throughout history.

Prerequisites

It's highly recommended that one should have basic concepts upon the followings before reading this review report:

  • how neural network works (forward pass, linear classifier, non-linear activation function)
  • how neural networks are trained (backward pass, SGD, batch)
  • specific operations (convolution, max pooling, avg. pooling, batch-norm)

Components in ConvNet

Before going into the whole network architecture, let's examine the major components in a convnet. This section is highly inspired by the book "Deep Learning" written by Goodfellow.

Convolution Layer

Motivation

  • Sparse connectivity
  • Parameter Sharing
  • Equivariance to translation

Important Concepts

  • Strided convolution = Convolution + downsampling
  • Special type: Locally connected convolutions (unshared convolution)

Non-linear Layer

  • Of course the most important, provides kernel trick
  • With conv+non-linear => exponential growth on linear regions

Why Pooling Layer

  • Max pooling:
    • Care more about whether some feature is present than exactly where it is
    • Invariant to small translations
    • Enlarges the maximum value's receptive field
  • Average pooling:
    • To reduce variance between hypothesys
  • Pooling over spatial vs. Pooling over features (may learn transformations)
  • Pooling is also useful for handling inputs of varying size

Why Normalization Layer

  • Re-arrange features in order to have better activations on non-linear layer

AlexNet

Innovations

  • 8-layer convnet
    • Use ReLU nonlinearity
    • Trained on 2 GPUs
    • Local Response Normalization (contrast normalization) => not used recently
  • Regularization
    • Data augmentation
    • Dropout

ZFNet

Innovations

  • Tried to visualize what AlexNet is actually learning => DeConv
  • Observing First layer problems
    • Few features dominates => renormalize weights with RMS exceeding 0.1
    • Aliasing and lack of mid-level features of AlexNet => Use a smaller filter size (11->7) and smaller stride (4->2)

Experiments

  • Feature invariance (to translation, scaling, rotation)
  • Occlusion Sensitivity (see if the machine is learning the object or its surroundings)
  • Correspondence Analysis (check whether network can learn a hierarchy structure)
  • Varying ImageNet model size and placing SVM on top => we must have a minimum depth

Overfeat

Innovations

  • More about object detection (sliding window)
  • Shows the meaning of 1x1 conv

VGG

Innovations

  • Investigate the effect of the convolutional network depth on its accuracy
  • Increase depth (16-19) with only 3x3 + 1x1 conv

Conclusions

  • Stacking 3x3 convs is better than single 7x7
    • faster computation
    • more non-linearity
  • Adding 1x1 conv in between may be useful => adding a non-linear layer (NIN has a better explanasion)
  • LRN is useless
  • Initialization is important (use Xavier init)
  • Add scaling to data augmentation

Network in Network

Concept

  • having too many filters for a single concept imposes extra burden on the next layer, which needs to consider all combinations of variations from the previous layer

Innovations

  • Represents MLP (multi-layer perceptron) Layers = larger conv + 1x1 conv
  • Global average pooling before fully connected rather than pure concatenate
  • MLP can implement dropouts on top

GoogLeNet

Concept

  • Improved utilization of the computing resources inside the network
  • Extend a network's width + depth instead of just depth
  • Based on Hebbian principle (sparsity + clustering): neurons that fires together, wires together
    • Want to create a somewhat non-uniform sparse model for each layer
    • Use concatenate of ConvNets => small dense matrices
    • Wires together => Clustering => use 1x1 Conv
    • Think convnets in another aspect: as clustering methods rather than feature extraction

Innovations

  • Inception module (1x1, 3x3, 5x5, 3x3 max)
  • Use 1x1 conv as a parameter reduction method (before 3x3, 5x5 conv; after 3x3 max as a clustering method)

Architecture

  • Use avg pooling at last
  • Tried to modify features produced in the middle of the network

ResNet

Innovations

  • Observed the problem of training error saturation (problems brought out by ReLU)
  • Use a Residual network
    • Gradients flow back from top
    • While network want an identity matrix, just set the non-linear functions to 0
  • Start using batch normalization

Architecture

  • Bottleneck architecture for speed up => read squeezenet

Future

  • Try different non-linear function methods
  • Think about bottleneck architecture
  • Track about GoogLeNet's spare representation