The recent rise of Self-Supervised Learning (SSL) as one of the preferred strategies for learning with limited labeled data, and abundant unlabeled data has led to the widespread use of these models. They are usually pretrained, finetuned, and evaluated on the same data distribution, i.e., within an in-distribution setting. However, they tend to perform poorly in out-of-distribution evaluation scenarios, a challenge that Unsupervised Domain Generalization (UDG) seeks to address. This paper introduces a novel method to standardize the styles of images in a batch. Batch styles standardization, relying on Fourier-based augmentations, promotes domain invariance in SSL by preventing spurious correlations from leaking into the features. The combination of batch styles standardization with the well-known contrastive-based method SimCLR leads to a novel UDG method named CLaSSy ($\textbf{C}$ontrastive $\textbf{L}$e$\textbf{a}$rning with $\textbf{S}$tandardized $\textbf{S}$t$\textbf{y}$les). CLaSSy offers serious advantages over prior methods, as it does not rely on domain labels and is scalable to handle a large number of domains. Experimental results on various UDG datasets demonstrate the superior performance of CLaSSy compared to existing UDG methods. Finally, the versatility of the proposed batch styles standardization is demonstrated by extending respectively the contrastive-based and non-contrastive-based SSL methods, SWaV and MSN, while considering different backbone architectures (convolutional-based, transformers-based).

翻译：近期，自监督学习（SSL）作为利用有限标注数据和大量无标注数据的一种首选策略，其兴起推动了这些模型的广泛应用。这些模型通常在同一数据分布（即域内设置）下进行预训练、微调和评估。然而，在域外评估场景中，它们的表现往往不佳，而这一问题正是无监督域泛化（UDG）试图解决的。本文提出了一种新方法，用于标准化批次中图像的风格。该方法基于傅里叶变换的增强技术，通过防止虚假相关性泄漏到特征中，促进了SSL中的域不变性。将批次风格标准化与著名的基于对比的方法SimCLR相结合，产生了一种新颖的UDG方法，命名为CLaSSy（$\textbf{C}$ontrastive $\textbf{L}$e$\textbf{a}$rning with $\textbf{S}$tandardized $\textbf{S}$t$\textbf{y}$les）。CLaSSy相比现有方法具有显著优势，因为它不依赖域标签，并且可扩展以处理大量域。在多个UDG数据集上的实验结果表明，与现有UDG方法相比，CLaSSy性能更优。最后，通过将所提出的批次风格标准化分别扩展到基于对比和非对比的SSL方法（SWaV和MSN），并考虑不同的骨干架构（基于卷积的、基于Transformer的），验证了该方法的通用性。