https://github.com/tim-learn/ATDOC
code released for our CVPR 2021 paper "Domain Adaptation with Auxiliary Target Domain-Oriented Classifier"
https://github.com/tim-learn/ATDOC
cvpr2021 domain-adaptation semi-supervised transfer-learning
Last synced: 5 months ago
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code released for our CVPR 2021 paper "Domain Adaptation with Auxiliary Target Domain-Oriented Classifier"
- Host: GitHub
- URL: https://github.com/tim-learn/ATDOC
- Owner: tim-learn
- License: mit
- Created: 2020-12-13T07:44:59.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2023-05-23T12:13:23.000Z (over 2 years ago)
- Last Synced: 2024-11-15T06:32:05.774Z (11 months ago)
- Topics: cvpr2021, domain-adaptation, semi-supervised, transfer-learning
- Language: Python
- Homepage:
- Size: 66.4 KB
- Stars: 55
- Watchers: 5
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Official implementation for **ATDOC**
[**[CVPR-2021] Domain Adaptation with Auxiliary Target Domain-Oriented Classifier**](https://arxiv.org/pdf/2007.04171.pdf)
[Update @ Nov 23 2021]
1. **[For Office, please change the *max-epoch* to 100; for VISDA-C, change the *max-epoch* to 1 and change the *net* to resnet101]**
2. **Add the code associated with SSDA, change the *max-epoch* to 20 for DomainNet-126**
3. **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]:
1. installing packages
`python == 3.6.8`
`pytorch ==1.1.0`
`torchvision == 0.3.0`
`numpy, scipy, sklearn, PIL, argparse, tqdm`
2. download the Office-Home dataset
`mkdir dataset`
`cd dataset`
`pip install gdown`
`gdown https://drive.google.com/u/0/uc?id=0B81rNlvomiwed0V1YUxQdC1uOTg&export=download`
`unzip OfficeHomeDataset_10072016.zip`
`mv ./OfficeHomeDataset_10072016/Real\ World ./OfficeHomeDataset_10072016/RealWorld`
`cd ../`
3. 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/`
4. 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/`
5. 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/`
6. 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/`
7. 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/`
8. 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/`
### Citation
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}
> }
>
### Contact
- [liangjian92@gmail.com](mailto:liangjian92@gmail.com)
- [dapeng.hu@u.nus.edu](mailto:dapeng.hu@u.nus.edu)
- [elefjia@nus.edu.sg](mailto:elefjia@nus.edu.sg)