Class-specific regularized joint distribution alignment for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) aims to learn robust classifiers for the target domain by leveraging knowledge from annotated source domain. Existing methods concentrated on cross-domain distribution alignment or domain-invariant deep learning model construction. Recently, although the class-specific characteristics have been incorporated for numerous UDA methods, there remains a need for further exploration in capturing intra-class and inter-class characteristics between the source and target domains. In this paper, we propose a novel UDA method, referred to as Class-specific Regularized Joint Distribution Alignment (CRJDA), to simultaneously optimize the intra-class distance of source domain, the inter-class discriminability of target domain, and the joint distribution between domains. Specifically, our method involves an overall optimization process that minimizes the maximum mean discrepancy while incorporating dual penalized class-specific regularizations during joint distribution alignment. Extensive experiments conducted on several benchmark datasets demonstrate that the superiority of the proposed method compared to both state-of-the-art conventional UDA methods and advanced deep UDA models.