Meta-causal

The code for Meta-causal Learning for Single Domain Generalization [CVPR2023]. Our code is based on the method of PDEN(https://github.com/lileicv/PDEN/).

Dataset

• Download the data and model from Baidu Cloud Disk (password:pxvt ).
• Place the dataset files in the path ./data/ and the model files in the path ./

Environment

Please refer to env.yaml

Train and Test

• For digit, run the command bash run_my_joint_test.sh 0 under the path ./run_digits/ .
• For PACS, when using art_painting as the source domain, run the command bash run_my_joint_v13_test.sh 0 under the path ./run_PACS/ .

If this code is helpful, please cite our paper

@InProceedings{Chen_2023_CVPR,
    author    = {Chen, Jin and Gao, Zhi and Wu, Xinxiao and Luo, Jiebo},
    title     = {Meta-Causal Learning for Single Domain Generalization},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {7683-7692}
}

Contact

gaozhi_2017@126.com