Optimal Transport for Domain Adaptation through Gaussian Mixture Models

This is the official repository for the paper Optimal Transport for Domain Adaptation through Gaussian Mixture Models, accepted in TMLR. Our paper uses the GMM-OTDA framework of (Delon and Desolneux, 2020) for domain adaptation, through 2 strategies,

• Mapping estimation, which maps points in the source domain towards the target domain using the GMMs,
• Label propagation, which estimates labels for the target domain GMM components.

You can run our code using,

python visda.py --base_path=PATH_TO_DATA --features="vit" --clusters_per_class="4" --reg_e=0.1

The extracted features for VisDA can be acessed in Google Drive.

Citation

@article{
    montesuma2024optimal,
    title={Optimal Transport for Domain Adaptation through Gaussian Mixture Models},
    author={Montesuma, Eduardo Fernandes and Mboula, Fred Maurice Ngol{\`e} and Souloumiac, Antoine},
    journal={Transactions on Machine Learning Research},
    year={2024},
    url={https://openreview.net/forum?id=DCAeXwLenB},
    note={Under review}
}

References

• Delon, J., & Desolneux, A. (2020). A Wasserstein-type distance in the space of Gaussian mixture models. SIAM Journal on Imaging Sciences, 13(2), 936-970.