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.

翻译：在自监督学习（SSL）中，模型通常在同一领域上进行预训练、微调和评估。然而，当在未见领域上进行评估时，它们的表现往往较差——这是无监督域泛化（UDG）试图解决的一个挑战。当前的UDG方法依赖于难以收集的域标签，以及面对大量领域时缺乏可扩展性的域特定架构，这使得现有方法既不实用又缺乏灵活性。受基于对比的UDG方法启发——这类方法通过限制对同一领域内样本的比较来减少虚假相关性——我们假设消除批次内的风格变异性，可以在无需域标签的情况下，以更便捷灵活的方式降低虚假相关性。为验证这一假设，我们提出批量风格标准化（BSS），这是一种相对简单但强大的基于傅里叶变换的方法，旨在标准化批次中图像的风格，专门设计用于与SSL方法集成以解决UDG问题。将BSS与现有SSL方法结合，相比之前的UDG方法具有显著优势：（1）它无需域标签或域特定的网络组件即可增强SSL表征的域不变性；（2）提供灵活性，因为BSS可以无缝集成到多种基于对比甚至非对比的SSL方法中。在多个UDG数据集上的实验表明，该方法显著提升了未见领域上的下游任务性能，通常优于或可与UDG方法相媲美。最后，本研究阐明了BSS在增强SSL表征域不变性及提升未见领域性能方面有效性的潜在机制。