Multiple Adaptation Network for Multi-Source and Multi-Target Domain Adaptation

Abstract

Multi-source domain adaptation (MSDA) has garnered significant attention due to its emphasis on transferring knowledge from multiple labeled source domains to a single unlabeled target domain. MSDA requires sufficient labeled data from multiple source domains, but in practice, massive unlabeled data exist instead of well-labeled data. Multiple target domains also provide plenty of information, which is useful for domain adaptation. However, most MSDA studies overlook the critical scenario of multi-source and multi-target domain adaptation (MMDA). To address these problems, we propose a Multiple Adaptation Network (MAN) approach for MMDA, which utilizes multiple alignment strategies for each source-target domain pair-group to align relevant specific feature spaces. MAN also aligns multiple classifiers for the relevant feature spaces to optimize the decision boundaries of multiple target domains. Moreover, to consider the task relations of multiple classifiers, we minimize the semantic differences between the target-conditioned classifiers and utilize a weight learning category to optimize this process. To fully utilize the information from multiple target domains, we transfer the style information of the target data to the source data, aiding in the training of multiple classifiers. Extensive experiments in challenge domain adaptation benchmarks, including the ImageCLEF-DA, Office-Home, DomainNet, and RGB-to-thermal datasets, demonstrate the superiority of our method over the state-of-the-art approaches.

Topics & Concepts