Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning

[Project]

Overview

1. Requirements

pip install -r requirements.txt

2. Data Preparation

As the same as the MCL.

3. Training

python -u train.py --dataset visda --base_path ./data/txt/visda/ --data_root /root/SSDA/data/visda/ --source clipart --target sketch --num 1 --log_dir ./logs --num_classes 12 --threshold2 0.4 --T 0.05

4. Acknowledgement

The code is partly based on MME and MCL.

5. Citation

@article{huang2023semi,
  title={Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning},
  author={Huang, Xinyang and Zhu, Chuang and Chen, Wenkai},
  journal={IJCAI},
  year={2023}
}

6. Contact

Xinyang Huang (hsinyanghuang7@gmail.com)

Chuang Zhu (czhu@bupt.edu.cn)

If you have any questions, you can contact us directly.