alexhuangjp / multiclassda

TPAMI2020 "Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice"

MultiClassDA (TPAMI2020)

Code release for "Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice", which is an extension of our preliminary work of SymmNets [Paper] [Code]

Please refer to the "run_temp.sh" for the usage. All expeimental results are logged in the file of "./experiments"

Included codes:

1. Codes of McDalNets -->./solver/McDalNet_solver.py
2. Codes of SymNets-V2
   1. For the Closed Set DA -->./solver/SymmNetsV2_solver.py
   2. For the Strongthened Closed Set DA -->./solver/SymmNetsV2SC_solver.py
   3. For the Partial DA -->./solver/SSymmNetsV2Partial_solver.py
   4. For the Open Set DA -->./solver/SymmNetsV2Open_solver.py

Dataset

The structure of the dataset should be like

Office-31
|_ 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
|  |_ ...
|_ ...

Citation

@inproceedings{zhang2019domain,
  title={Domain-symmetric networks for adversarial domain adaptation},
  author={Zhang, Yabin and Tang, Hui and Jia, Kui and Tan, Mingkui},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5031--5040},
  year={2019}
}
@article{zhang2020unsupervised,
  title={Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice},
  author={Zhang, Yabin and Deng, Bin and Tang, Hui and Zhang, Lei and Jia, Kui},
  journal=IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2020}
  publisher={IEEE}
}

Contact

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

• [email protected]

or describe your problem in Issues.