Semantic Concentration for Domain Adaptation

Code release for "Semantic Concentration for Domain Adaptation" (ICCV 2021)

Paper

Semantic Concentration for Domain Adaptation (ICCV 2021)

We propose Semantic Concentration for Domain Adaptation (SCDA), which encourages the model to concentrate on the most principal features via the pair-wise adversarial alignment of prediction distributions

Requirements

The model is trained on a NVIDIA RTX 2080Ti GPU.

• python 3.7
• torch >= 1.7.0
• torchvision >= 0.8.1
• PIL >= 7.2.0
• numpy
• argparse

Datasets

Office-31

Office-31 dataset can be found here.

Office-Home

Office-Home dataset can be found here.

DomainNet

DomainNet dataset can be found here.

Running the code

Train on DomainNet with ResNet-101

python3 train_SCDA.py --gpu_id 2 --arch resnet101 --dset domainnet --output_dir log/SCDA/domainnet --s_dset_path data/list/DomainNet/clipart_train.txt --t_dset_path data/list/DomainNet/infograph_train.txt --t_test_path data/list/DomainNet/infograph_test.txt --epochs 30 --iters-per-epoch 1000 --pdd_tradeoff 1.0 --MI_tradeoff 0.1 --temp 10.0 --threshold 0.8 --lr 0.01

Train on Office-Home with ResNet-50

python3 train_SCDA.py --gpu_id 2 --arch resnet50 --dset office-home --output_dir log/SCDA/home --s_dset_path data/list/OfficeHome/Art_65.txt --t_dset_path data/list/OfficeHome/Clipart_65.txt --epochs 40 --iters-per-epoch 500 --pdd_tradeoff 1.0 --MI_tradeoff 0.1 --temp 10.0 --threshold 0.8 --lr 0.01

Train on Office-31 with ResNet-50

python3 train_SCDA.py --gpu_id 2 --arch resnet50 --dset office --output_dir log/SCDA/office31 --s_dset_path data/list/office31/amazon_31.txt --t_dset_path data/list/office31/webcam_31.txt --epochs 40 --iters-per-epoch 500 --pdd_tradeoff 1.0 --MI_tradeoff 0.1 --temp 10.0 --threshold 0.8 --lr 0.01

Evaluate

Several pre-trained models of SCDA can be downloaded here and put in <root_dir>/Checkpoint

evaluate on Office-31

python3 evaluate.py --gpu_id 2 --arch resnet50 --dset office --t_test_path data/list/office31/webcam_31.txt --weight_path Checkpoint/amazon-webcam.pth.zip

Citation

If you find this code useful for your research, please cite our paper:

@inproceedings{Li2021SCDA,
    title = {Semantic Concentration for Domain Adaptation},
    author = {Li, Shuang and Xie, Mixue and Lv, Fangrui and Liu, Chi Harold and Liang, Jian and Qin, Chen and Li, Wei},
    booktitle = {ICCV},   
    year = {2021}
}

Acknowledgements

Some codes are adapted from Transfer-Learning-Library. We thank them for their excellent projects.

Contact

If you have any problem about our code, feel free to contact

• shuangli@bit.edu.cn
• michellexie102@gmail.com
• fangruilv@bit.edu.cn

or describe your problem in Issues.