Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian
  Mixture Models and Optimal Transport
Eduardo Fernandes Montesuma Fred Ngolè Mboula Antoine Souloumiac
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-WBT and GMM-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.