Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport

• URL: http://arxiv.org/abs/2006.12938v2
• Date: Thu, 2 Jun 2022 14:03:56 GMT
• Title: Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport
• Authors: Rosanna Turrisi, R\'emi Flamary, Alain Rakotomamonjy, Massimiliano Pontil
• Abstract summary: We propose a new approach to domain adaptation on unlabeled target datasets. 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.
• Score: 35.37752209765114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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