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Open Compound Domain Adaptation (OCDA) is the author's re-implementation of the compound domain adaptator described in:
"Open Compound Domain Adaptation"
Ziwei Liu^*,  Zhongqi Miao^*,  Xingang Pan,  Xiaohang Zhan,  Dahua Lin,  Stella X. Yu,  Boqing Gong  (CUHK & Berkeley & Google)  in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020, Oral Presentation

Further information please contact Zhongqi Miao and Ziwei Liu.

• PyTorch (version >= 0.4.1)
• scikit-learn

• 11/09/2020: We have uploaded C-Faces dataset. Corresponding codes will be updated shortly. Please be patient. Thank you very much!
• 06/16/2020: We have released C-Digits dataset and corresponding weights.

[OCDA Datasets]

First, please download C-Digits, save it to a directory, and change the dataset root in the config file accordingly. The file contains MNIST, MNIST-M, SVHN, SVHN-bal, and SynNum.

For C-Faces, please download Multi-PIE first. Since it is a proprietary dataset, we can only privide the data list we used during training here. We will update the dataset function accordingly.

To run experiments for both training and evaluation on the C-Digits datasets (SVHN -> Multi):

python main.py --config ./config svhn_bal_to_multi.yaml

After training is completed, the same command will automatically evaluate the trained models.

• We will be releasing code for C-Faces experiements very soon.

• Please refer to: https://github.com/XingangPan/OCDA-Driving-Example .

NOTE: All reproduced weights need to be decompressed into results directory:

OpenCompoundedDomainAdaptation-OCDA
    |--results

The use of this software is released under BSD-3.

@inproceedings{compounddomainadaptation,
  title={Open Compound Domain Adaptation},
  author={Liu, Ziwei and Miao, Zhongqi and Pan, Xingang and Zhan, Xiaohang and Lin, Dahua and Yu, Stella X. and Gong, Boqing},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}