The problem of domain adaptation on an unlabeled target dataset using
knowledge from multiple labelled source datasets is becoming increasingly
important. A key challenge is to design an approach that overcomes the
covariate and target shift both among the sources, and between the source and
target domains. In this paper, we address this problem from a new perspective:
instead of looking for a latent representation invariant between source and
target domains, we exploit the diversity of source distributions by tuning
their weights to the target task at hand. Our method, named Weighted Joint
Distribution Optimal Transport (WJDOT), aims at finding simultaneously an
Optimal Transport-based alignment between the source and target distributions
and a re-weighting of the sources distributions. We discuss the theoretical
aspects of the method and propose a conceptually simple algorithm. Numerical
experiments indicate that the proposed method achieves state-of-the-art
performance on simulated and real-life datasets.

本文介绍了一种新的方法 —— 加权联合分布最优输运，它利用源分布的多样性来解决多个标记源数据集到未标记目标数据集的领域适应问题，同时在源和目标分布之间寻找最优的输运对齐和重量重新平衡。 数值实验表明，该方法在模拟和现实数据集上实现了最先进的性能。