Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation

This is the implementation of Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation in Pytorch.

Getting Started

Installation

pip install -r requirements.txt

Download dataset

Download the dataset and checkpoint from the model_and_data_link:

Extract the model and data to the root folder of EDH project. Follow the folder structure below:

-checkpoint
    -EDHKD********.pt
    -*****
-data
    -DSADS
        -pseudo_dataset
            -0_sensor_0.npy
            -***
        -0.mat
        -***
    -ENABL3S
        -pseudo_dataset
            -0_sensor_0.npy
            -***
        -AB_0.mat
        -***

Test

python main.py --eval_only True

Train

python main.py --eval_only False

Contact

For more related works and codes, please view my homepage: https://sites.google.com/view/kuangenzhang

Further information please contact Kuangen Zhang (kuangen@student.ubc.ca).

Citation

If you find our work useful in your research, please consider citing:

@article{zhang2022ensemble,
  title={Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation},
  author={Zhang, Kuangen and Chen, Jiahong and Wang, Jing and Chen, Xinxing and Leng, Yuquan and de Silva, Clarence W and Fu, Chenglong},
  journal={arXiv preprint arXiv:2204.07308},
  year={2022}
}

Acknowledgement

We acknowledge that we borrow a part of code from MCD_DA.

Reference

• K. Saito, K. Watanabe, Y. Ushiku, and T. Harada, “Maximum classifier discrepancy for unsupervised domain adaptation,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, Jun. 2018, pp. 3723–3732.