A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition

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Code for WMDD: A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition. This paper extends the margin disparity discrepancy (MDD) theory in single-source unsupervised domain adaptation (UDA) to the multi-source UDA, and proposes a novel weighted multi-source UDA method, named WMDD, for HMI recognition. The implementation is based on MDD and EDHKD.

Installation

Install Python packages listed in WMDD.yaml.

conda env create -f environment.yaml
conda activate WMDD

Training

The hyperparameters are automatically loaded from configs.

python run.py --eval_only False

Testing

Just run run.py with specifying the task name.

python run.py --eval_only True

Citation

If you find WMDD helpful for your work, please cite:

@article{liu2024weight,
  title={A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition},
  author={Liu, Xiao-Yin and Li, Guotao and Zhou, Xiao-Hu and Liang, Xu and Hou, Zeng-Guang},
  journal={arXiv preprint arXiv:2404.15366},
  year={2024}
}