Disentangling Masked Autoencoders for Unsupervised Domain Generalization

An Zhang, Han Wang, Xiang Wang, Tat-Seng Chua

Abstract: Domain Generalization (DG), designed to enhance out-of-distribution (OOD) generalization, is all about learning invariance against domain shifts utilizing sufficient supervision signals. Yet, the scarcity of such labeled data has led to the rise of unsupervised domain generalization (UDG)—a more important yet challenging task in that models are trained across diverse domains in an unsupervised manner and eventually tested on unseen domains. UDG is fast gaining attention but is still far from well-studied.