  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. To close the
research gap, we propose a novel learning framework designed for UDG, termed
the Disentangled Masked Auto Encoder (DisMAE), aiming to discover the
disentangled representations that faithfully reveal the intrinsic features and
superficial variations without access to the class label. At its core is the
distillation of domain-invariant semantic features, which cannot be
distinguished by domain classifier, while filtering out the domain-specific
variations (for example, color schemes and texture patterns) that are unstable
and redundant. Notably, DisMAE co-trains the asymmetric dual-branch
architecture with semantic and lightweight variation encoders, offering dynamic
data manipulation and representation level augmentation capabilities. Extensive
experiments on four benchmark datasets (i.e., DomainNet, PACS, VLCS, Colored
MNIST) with both DG and UDG tasks demonstrate that DisMAE can achieve
competitive OOD performance compared with the state-of-the-art DG and UDG
baselines, which shed light on potential research line in improving the
generalization ability with large-scale unlabeled data.
