Class-aware sample reweighting optimal transport for multi-source domain adaptation

Abstract

Multi-Source Domain Adaptation (MSDA) techniques have attracted widespread attention due to their availability to transfer knowledge from multiple source domains to the unlabeled target domain. Optimal transport (OT) has recently been utilized to measure the distance between distributions in virtue of its robustness. This paper proposes a novel OT-based Class-Aware Sample Reweighting (CASR) method to achieve sample-level fine-grained alignment between multi-source and target. Technically, the class-aware sampling strategy ensures class-level conditional alignment during transport by explicitly selecting samples from each domain. Besides, the sample-reweighting module is designed to allocate specific mass to each transmitted sample, which considers the classification reliability and the spatial information correlation to obtain the alignment priority between target and multi-source and further optimize the transport plan. Extensive experiments conducted on several benchmarks show that CASR presents significant advantages compared with other MSDA methods, and the visualization analysis further demonstrates the effectiveness of each proposed module.