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. Nevertheless, 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
overlapped, and the target domain is allowed to contain novel categories 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 category shifts across the source and target domains. To address this
issue, we propose a variational domain disentanglement (VDD) framework, which
decomposes the domain representations and semantic features for each instance
by encouraging dimension-wise independence. To identify the target samples 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 experiments conducted on two benchmark datasets
demonstrate the validity of the proposed framework.

本研究提出了一种多源领域适应方法 (GM-DA)，试图解决传统方法中源域与目标域之间类别不完全重叠的问题，该方法基于变分域解缠绕 (VDD) 框架，借助在线伪标签来识别未知类别的目标样本，并且在两个基准数据集上展现了该框架的有效性。