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Contrastive Adaptation Network

Update: 2020-10-17: We have extended our method to the multi-source domain adaptation scenario. Please refer to our TPAMI paper Contrastive Adaptation Network for Single- and Multi-Source Domain Adaptation for more details. We will release our code for the multi-source domain adaptation soon.

2019-11: This is the Pytorch implementation for our CVPR 2019 paper Contrastive Adaptation Network for Unsupervised Domain Adaptation. As we reorganized our code based on a new pytorch version, some hyper-parameters are slightly different from the paper.

Requirements

  • Python 3.7
  • Pytorch 1.1
  • PyYAML 5.1.1

Dataset

The structure of the dataset should be like

Office-31
|_ category.txt
|_ amazon
|  |_ back_pack
|     |_ <im-1-name>.jpg
|     |_ ...
|     |_ <im-N-name>.jpg
|  |_ bike
|     |_ <im-1-name>.jpg
|     |_ ...
|     |_ <im-N-name>.jpg
|  |_ ...
|_ dslr
|  |_ back_pack
|     |_ <im-1-name>.jpg
|     |_ ...
|     |_ <im-N-name>.jpg
|  |_ bike
|     |_ <im-1-name>.jpg
|     |_ ...
|     |_ <im-N-name>.jpg
|  |_ ...
|_ ...

The "category.txt" contains the names of all the categories, which is like

back_pack
bike
bike_helmet
...

Training

./experiments/scripts/train.sh ${config_yaml} ${gpu_ids} ${adaptation_method} ${experiment_name}

For example, for the Office-31 dataset,

./experiments/scripts/train.sh ./experiments/config/Office-31/CAN/office31_train_amazon2dslr_cfg.yaml 0 CAN office31_a2d

for the VisDA-2017 dataset,

./experiments/scripts/train.sh ./experiments/config/VisDA-2017/CAN/visda17_train_train2val_cfg.yaml 0 CAN visda17_train2val

The experiment log file and the saved checkpoints will be stored at ./experiments/ckpt/${experiment_name}

Test

./experiments/scripts/test.sh ${config_yaml} 0 ${if_adapted} ${experiment_name}

Example:

./experiments/scripts/test.sh ./experiments/config/Office-31/office31_test_amazon_cfg.yaml 0 True visda17_test

Citing

Please cite our paper if you use our code in your research:

@article{kangcontrastive,
  title={Contrastive Adaptation Network for Single-and Multi-Source Domain Adaptation},
  author={Kang, Guoliang and Jiang, Lu and Wei, Yunchao and Yang, Yi and Hauptmann, Alexander G},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2020}
}

@inproceedings{kang2019contrastive,
  title={Contrastive Adaptation Network for Unsupervised Domain Adaptation},
  author={Kang, Guoliang and Jiang, Lu and Yang, Yi and Hauptmann, Alexander G},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4893--4902},
  year={2019}
}

Contact

If you have any questions, please contact me via kgl.prml@gmail.com.

Thanks to third party

The way of setting configurations is inspired by https://github.com/rbgirshick/py-faster-rcnn.

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pytorch implementation for Contrastive Adaptation Network

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