[CVPR-2021] Domain Adaptation with Auxiliary Target Domain-Oriented Classifier
[Update @ Nov 23 2021]
- [For Office, please change the max-epoch to 100; for VISDA-C, change the max-epoch to 1 and change the net to resnet101]
- Add the code associated with SSDA, change the max-epoch to 20 for DomainNet-126
- Thank @lyxok1 for pointing out the typo in Eq.(6), we have corrected it in the new verison of this paper.
Below is the demo for ATDOC on a UDA task of Office-Home [max_epoch to 50]:
-
installing packages
python == 3.6.8pytorch ==1.1.0torchvision == 0.3.0numpy, scipy, sklearn, PIL, argparse, tqdm -
download the Office-Home dataset
mkdir datasetcd datasetpip install gdowngdown https://drive.google.com/u/0/uc?id=0B81rNlvomiwed0V1YUxQdC1uOTg&export=downloadunzip OfficeHomeDataset_10072016.zipmv ./OfficeHomeDataset_10072016/Real\ World ./OfficeHomeDataset_10072016/RealWorldcd ../ -
run the main file with 'Source-model-only'
python demo_uda.py --pl none --dset office-home --max_epoch 50 --s 0 --t 1 --gpu_id 0 --method srconly --output logs/uda/run1/ -
run the main file with 'ATDOC-NC'
python demo_uda.py --pl atdoc_nc --tar_par 0.1 --dset office-home --max_epoch 50 --s 0 --t 1 --gpu_id 0 --method srconly --output logs/uda/run1/ -
run the main file with 'ATDOC-NA'
python demo_uda.py --pl atdoc_na --tar_par 0.2 --dset office-home --max_epoch 50 --s 0 --t 1 --gpu_id 0 --method srconly --output logs/uda/run1/ -
run the main file with 'ATDOC-NA' combined with 'CDAN+E'
python demo_uda.py --pl atdoc_na --tar_par 0.2 --dset office-home --max_epoch 50 --s 0 --t 1 --gpu_id 0 --method CDANE --output logs/uda/run1/ -
run the main file with 'ATDOC-NA' combined with 'MixMatch'
python demo_mixmatch.py --pl none --dset office-home --max_epoch 50 --s 0 --t 1 --gpu_id 0 --output logs/uda/run1/ -
run the main file with 'ATDOC-NA' combined with 'MixMatch'
python demo_mixmatch.py --pl atdoc_na --dset office-home --max_epoch 50 --s 0 --t 1 --gpu_id 0 --output logs/uda/run1/
If you find this code useful for your research, please cite our paper
@inproceedings{liang2021domain,
title={Domain Adaptation with Auxiliary Target Domain-Oriented Classifier},
author={Liang, Jian and Hu, Dapeng and Feng, Jiashi},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2021}
}