KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation (Accepted at ICML 2021)

Here is the official implementation of the model KD3A in paper KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation.

Model Review

• Knowledge Distillation

  

• Knowledge Vote

  

Setup

Install Package Dependencies

Python Environment: >= 3.6
torch >= 1.2.0
torchvision >= 0.4.0
tensorbard >= 2.0.0
numpy
yaml

Install Datasets

We need users to declare a base path to store the dataset as well as the log of training procedure. The directory structure should be

base_path
│       
└───dataset
│   │   DigitFive
│       │   mnist_data.mat
│       │   mnistm_with_label.mat
|       |   svhn_train_32x32.mat  
│       │   ...
│   │   DomainNet
│       │   ...
│   │   OfficeCaltech10
│       │   ...
|   |   Office31
|       |   ...
|   |   AmazonReview
|       |   ...
└───trained_model_1
│   │	parmater
│   │	runs
└───trained_model_2
│   │	parmater
│   │	runs
...
└───trained_model_n
│   │	parmater
│   │	runs    

Our framework now support five multi-source domain adaptation datasets: DigitFive, DomainNet, AmazonReview, OfficeCaltech10 and Office31.

• DigitFive

  The DigitFive dataset can be accessed in Google Drive.

• DomainNet

  VisDA2019 provides the DomainNet dataset.

• AmazonReview

  The AmazonReview dataset can be accessed in Google Drive.

Unsupervised Multi-source Domain Adaptation

The configuration files can be found under the folder ./config, and we provide four config files with the format .yaml. To perform the unsupervised multi-source decentralized domain adaptation on the specific dataset (e.g., DomainNet), please use the following commands:

python main.py --config DomainNet.yaml --target-domain clipart -bp base_path

The training process for DomainNet is as follows.

Negative Transfer

In training process, our model will record the domain weights as well as the accuracy for target domain as

Source Domains  :['infograph', 'painting', 'quickdraw', 'real', 'sketch']

Domain Weight : [0.1044, 0.3263, 0.0068, 0.2531, 0.2832]

Target Domain clipart Accuracy Top1 : 0.726 Top5: 0.902

• Irrelevant Domains

  We view quickdraw as the irrelevant domain, and the K3DA assigns low weights to it in training process.

• Malicious Domains

  We use the poisoning attack with level $m%$ to create malicious domains. The related settings in the configuration files is as follows:

  UMDAConfig:
      malicious:
        attack_domain: "real"
        attack_level: 0.3
  

  With this setting, we will perform poisoning attack in the source domain real with $30%$ mislabeled samples.

Communication Rounds

We also provide the settings in .yaml config files to perform model aggregation with communication rounds $r$ as follows:

UMDAConfig:
    communication_rounds: 1

The communication rounds can be set into $[0.2, 0.5 , 1 , ... , N]$.

Reference

If you find this useful in your work please consider citing:

@InProceedings{pmlr-v139-feng21f,
  title = 	 {KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation},
  author =       {Feng, Haozhe and You, Zhaoyang and Chen, Minghao and Zhang, Tianye and Zhu, Minfeng and Wu, Fei and Wu, Chao and Chen, Wei},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {3274--3283},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR}
}