MetaVisionLab / SSDG

Semi-Supervised Domain Generalization

Semi-Supervised Domain Generalization with Evolving Intermediate Domain

paper

Requirements

• numpy==1.19.2
• Pillow==8.1.0
• PyYAML==5.3.1
• scipy==1.9.3
• scikit_learn==1.1.1
• six==1.15.0
• torch==1.7.1
• torchvision==0.8.2
• Ubuntu==18.04
• Python==3.8.5

Installation

Local install

git clone https://github.com/MetaVisionLab/SSDG.git
cd SSDG
pip install -r requirements.txt

Or using docker image pytorch/pytorch:1.7.1-cuda11.0-cudnn8-devel.

Data Preparation

You can download the dataset PACS, Digits-DG, Office-Home-DG, web_pacs and web_office to the folder SSDG/data and unzip it, which include three folders representing three datasets in our paper.

File structure:

SSDG/data
|--digits_dg/
  |–– images/
  |–– splits/
|--office_home_dg/
  |–– images/
  |–– splits/
|--pacs/
  |–– images/
  |–– splits/
|--web_office/
|--web_pacs/

Training

bash script/pacs.sh       #train pacs
bash script/digits.sh     #train digits_dg
bash script/officeHome.sh   #train office_home_dg
...

Validation

We evaluate our method on all the SSDG tasks for each dataset and report the average accuracy.For each task(for example A2C),we report its average accuracy on last 5 epoch.

cd tools/
python parse_test_res_single.py log/UDAG_A2C.log --test-log

Citation

Please cite our paper:

@article{lin2024semi,
  title={Semi-supervised domain generalization with evolving intermediate domain},
  author={Lin, Luojun and Xie, Han and Sun, Zhishu and Chen, Weijie and Liu, Wenxi and Yu, Yuanlong and Zhang, Lei},
  journal={Pattern Recognition},
  volume={149},
  pages={110280},
  year={2024},
  publisher={Elsevier}
}

Contact us

For any questions, please feel free to contact Han Xie or Dr. Luojun Lin.

Copyright

This code is free to the academic community for research purpose only. For commercial purpose usage, please contact Dr. Luojun Lin.