Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport
<|reference_start|>Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport: In this paper, we tackle Multi-Source Domain Adaptation (MSDA), a task in transfer learning where one adapts multiple heterogeneous, labeled source probability measures towards a different, unlabeled target measure. We propose a novel framework for MSDA, based on Optimal Transport (OT) and Gaussian Mixture Models (GMMs). Our framework has two key advantages. First, OT between GMMs can be solved efficiently via linear programming. Second, it provides a convenient model for supervised learning, especially classification, as components in the GMM can be associated with existing classes. Based on the GMM-OT problem, we propose a novel technique for calculating barycenters of GMMs. Based on this novel algorithm, we propose two new strategies for MSDA: GMM-Wasserstein Barycenter Transport (WBT) and GMM-Dataset Dictionary Learning (DaDiL). We empirically evaluate our proposed methods on four benchmarks in image classification and fault diagnosis, showing that we improve over the prior art while being faster and involving fewer parameters. Our code is publicly available at https://github.com/eddardd/gmm_msda<|reference_end|>
@article{montesuma2024lighter,,
    title={Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian
  Mixture Models and Optimal Transport},
    author={Eduardo Fernandes Montesuma, Fred Ngol`e Mboula, Antoine Souloumiac},
    journal={arXiv preprint arXiv:2404.10261},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10261},
    primaryClass={stat.ML cs.LG}
}