| # 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](https://pan.baidu.com/s/14pdVbNAHWKeC4AE7QqtFmw) (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 | |