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.

翻译：领域泛化旨在通过利用充分的监督信号学习对领域偏移的不变性，从而增强分布外泛化能力。然而，标记数据的稀缺催生了无监督领域泛化这一更重要且更具挑战性的任务——模型需在无监督条件下跨多个领域进行训练，并最终在未见领域上进行测试。该任务正迅速获得关注，但尚未得到充分研究。为填补这一研究空白，我们提出了一种专为无监督领域泛化设计的新型学习框架——解耦掩码自编码器，其目标是在无需类别标签的情况下，发现能够忠实揭示内在特征与表层变化的解耦表征。其核心在于蒸馏领域不变的语义特征（这些特征无法被领域分类器区分），同时滤除不稳定且冗余的领域特定变化（例如配色方案与纹理模式）。值得注意的是，该框架通过语义编码器与轻量化变化编码器协同训练非对称双分支架构，提供了动态数据操纵与表征级增强能力。在四个基准数据集上开展的领域泛化与无监督领域泛化实验表明，相较于最先进的基线方法，该框架能够实现具有竞争力的分布外性能，这为利用大规模无标记数据提升泛化能力的研究方向提供了新的启示。