/CAiDA

Code for CAiDA

Primary LanguagePython

PyTorch Implementation for CAiDA

[NeurIPS-2021] Confident Anchor-Induced Multi-Source Free Domain Adaptation

This is the implementation code of our paper "Confident Anchor-Induced Multi-Source Free Domain Adaptation" accepted by NeurIPS-2021.

Overview of The CAiDA Model

overview

Requirements:

  • python == 3.6.8
  • pytorch == 1.1.0
  • numpy == 1.17.4
  • torchvision == 0.3.0
  • scipy == 1.3.1
  • sklearn == 0.5.0
  • argparse, PIL

Datasets Preparation:

  • Office Dataset: Download the datasets Office-31, Office-Home, Office-Caltech from the official websites.
  • Digits-Five Dataset: Download the datasets MNIST, MNIST-M, USPS, SVHN, Synthetic Digits from the official websites.
  • DomainNet Dataset: Download DomainNet from the official website.
  • Place these datasets in './data'.
  • Using gen_list.py to generate '.txt' file for each dataset (change dataset argument in the file accordingly).

Training:

  • Train source models (shown here for Office with source A)
python train_source.py --dset office-31 --s 0 --max_epoch 100 --trte val --gpu_id 0 --output ckps/source/
  • Adapt to target domain (shown here for Office with target D)
python train_target_CAiDA.py --dset office-31 --t 1 --max_epoch 15 --gpu_id 0 --cls_par 0.7 --crc_par 0.01 --output_src ckps/source/ --output ckps/CAiDA

Citation:

  • If you find this code is useful to your research, please consider to cite our paper.
@inproceedings{NEURIPS2021_Dong,
 author = {Dong, Jiahua and Fang, Zhen and Liu, Anjin and Sun, Gan and Liu, Tongliang},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {2848--2860},
 publisher = {Curran Associates, Inc.},
 title = {Confident Anchor-Induced Multi-Source Free Domain Adaptation},
 volume = {34},
 year = {2021}
}

@ARTICLE{TPAMI2021_Dong,
  author={Dong, Jiahua and Cong, Yang and Sun, Gan and Fang, Zhen and Ding, Zhengming},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation}, 
  year={2021},
}

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