  In Self-Supervised Learning (SSL), models are typically pretrained,
fine-tuned, and evaluated on the same domains. However, they tend to perform
poorly when evaluated on unseen domains, a challenge that Unsupervised Domain
Generalization (UDG) seeks to address. Current UDG methods rely on domain
labels, which are often challenging to collect, and domain-specific
architectures that lack scalability when confronted with numerous domains,
making the current methodology impractical and rigid. Inspired by
contrastive-based UDG methods that mitigate spurious correlations by
restricting comparisons to examples from the same domain, we hypothesize that
eliminating style variability within a batch could provide a more convenient
and flexible way to reduce spurious correlations without requiring domain
labels. To verify this hypothesis, we introduce Batch Styles Standardization
(BSS), a relatively simple yet powerful Fourier-based method to standardize the
style of images in a batch specifically designed for integration with SSL
methods to tackle UDG. Combining BSS with existing SSL methods offers serious
advantages over prior UDG methods: (1) It eliminates the need for domain labels
or domain-specific network components to enhance domain-invariance in SSL
representations, and (2) offers flexibility as BSS can be seamlessly integrated
with diverse contrastive-based but also non-contrastive-based SSL methods.
Experiments on several UDG datasets demonstrate that it significantly improves
downstream task performances on unseen domains, often outperforming or rivaling
with UDG methods. Finally, this work clarifies the underlying mechanisms
contributing to BSS's effectiveness in improving domain-invariance in SSL
representations and performances on unseen domain.
