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Deep learning-based multi-source unsupervised domain adaptation (MUDA) hasbeen actively studied in recent years. Compared with single-source unsuperviseddomain adaptation (SUDA), domain shift in MUDA exists not only between thesource and target domains but also among multiple source domains. Most existingMUDA algorithms focus on extracting domain-invariant representations among alldomains whereas the task-specific decision boundaries among classes are largelyneglected. In this paper, we propose an end-to-end trainable network thatexploits domain Consistency Regularization for unsupervised Multi-source domainAdaptive classification (CRMA). CRMA aligns not only the distributions of eachpair of source and target domains but also that of all domains. For each pairof source and target domains, we employ an intra-domain consistency toregularize a pair of domain-specific classifiers to achieve intra-domainalignment. In addition, we design an inter-domain consistency that targetsjoint inter-domain alignment among all domains. To address differentsimilarities between multiple source domains and the target domain, we designan authorization strategy that assigns different authorities to domain-specificclassifiers adaptively for optimal pseudo label prediction and self-training.Extensive experiments show that CRMA tackles unsupervised domain adaptationeffectively under a multi-source setup and achieves superior adaptationconsistently across multiple MUDA datasets.

Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification