Partially-Labeled Domain Generalization via Multi-Dimensional Domain Adaptation

Domain generalization deals with a challenging setting where several labeled source domains are given, and the goal is to train machine learning models that can generalize to an unseen test domain. However, in practice, labeled samples are often difficult and expensive to obtain. Thus the source domains would not always be labeled. When only some source domains are labeled and others are unlabeled, we formally introduce this domain generalization problem as Partially-Labeled Domain Generalization (PLDG). In this paper, we study the most challenging setting in PLDG problems, where only one source domain is labeled and a few unlabeled source domains are available. To enable generalization, we assume that all source domains follow certain domain index information that can reflect their domain relationships. With this domain index information, we propose a Multi-Dimensional Domain Adaptation (MDDA) method to address this PLDG problem. Specifically, the MDDA method first trains multiple domain adaptation models to adapt from the labeled source domain to all the unlabeled source domains via adversarial learning. Then those domain adaptation models and the source-only model trained on the labeled source domain only are distilled into the target model used for the unseen target domain. Theoretically, we provide a generalization bound of the MDDA method. The experiments on four real-world datasets demonstrate the effectiveness of the proposed MDDA method.