Discovering Domain Disentanglement for Generalized Multi-Source Domain Adaptation

Abstract

A typical multi-source domain adaptation (MSDA) approach aims to transfer knowledge learned from a set of labeled source domains, to an unlabeled target domain. Neverthe-less, prior works strictly assume that each source domain shares the identical group of classes with the target domain, which could hardly be guaranteed as the target label space is not observable. In this paper, we consider a more versatile setting of MSDA, namely Generalized Multi-source Domain Adaptation, wherein the source domains are partially over-lapped, and the target domain is allowed to contain novel cat-egories that are not presented in any source domains. This new setting is more elusive than any existing domain adaptation protocols due to the coexistence of the domain and cate-gory shifts across the source and target domains. To address this issue, we propose a variational domain disentanglement (VDD) framework, which decomposes the domain represen-tations and semantic features for each instance by encouraging dimension-wise independence. To identify the target sam-ples of unknown classes, we leverage online pseudo labeling, which assigns the pseudo-labels to unlabeled target data based on the confidence scores. Quantitative and qualitative experi-ments conducted on two benchmark datasets demonstrate the validity of the proposed framework.