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：自监督学习（SSL）作为有限标注数据与丰富无标注数据场景下的首选学习策略，近年来兴起，推动了这些模型的广泛应用。这类模型通常在同一数据分布内（即领域内设置）进行预训练、微调和评估，但在领域外评估场景中往往表现不佳，这正是无监督域泛化（UDG）试图解决的挑战。本文提出一种创新方法，用于批量标准化图像风格。基于傅里叶变换的批次风格标准化通过防止虚假相关性渗入特征，从而促进SSL中的域不变性。将批次风格标准化与经典的对比学习方法SimCLR相结合，提出了一种新型UDG方法——CLaSSy（$\textbf{C}$ontrastive $\textbf{L}$e$\textbf{a}$rning with $\textbf{S}$tandardized $\textbf{S}$t$\textbf{y}$les）。与现有方法相比，CLaSSy具有显著优势：无需依赖域标签，且可扩展至处理大量域。在多个UDG数据集上的实验结果表明，CLaSSy的性能优于现有UDG方法。最后，通过将所提批次风格标准化分别扩展至基于对比与基于非对比的SSL方法（SWaV和MSN），并考虑不同的骨干架构（卷积型、Transformer型），验证了该方法的多功能性。