Awesome

Open Compound Domain Adaptation

[Project] [Paper] [Demo] [Blog]

Overview

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.

Requirements

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

Updates:

• 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.

Data Preparation

[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.

Getting Started (Training & Testing)

C-Digits

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.

C-Faces

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

C-Driving

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

Reproduced Benchmarks and Model Zoo

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

OpenCompoundedDomainAdaptation-OCDA
    |--results

C-Digits (Results may currently have variations.)

License and Citation

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}
}