Domain generalization (DG) approaches intend to extract domain invariant features that can lead to a more robust deep learning model. In this regard, style augmentation is a strong DG method taking advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains. While it is one of the state-of-the-art methods, prior works on style augmentation have either disregarded the interdependence amongst distinct feature channels or have solely constrained style augmentation to linear interpolation. To address these research gaps, in this work, we introduce a novel augmentation approach, named Correlated Style Uncertainty (CSU), surpassing the limitations of linear interpolation in style statistic space and simultaneously preserving vital correlation information. Our method's efficacy is established through extensive experimentation on diverse cross-domain computer vision and medical imaging classification tasks: PACS, Office-Home, and Camelyon17 datasets, and the Duke-Market1501 instance retrieval task. The results showcase a remarkable improvement margin over existing state-of-the-art techniques. The source code is available https://github.com/freshman97/CSU.

翻译：域泛化方法旨在提取域不变特征，从而提升深度学习模型的鲁棒性。在该领域中，风格增强是一种高效的域泛化方法，它利用包含丰富风格信息的实例级特征统计量来合成新域。尽管风格增强已成为当前最先进方法之一，但现有相关研究要么忽略了不同特征通道之间的相互依赖性，要么仅将风格增强局限于线性插值。针对这些研究空白，本文提出一种名为“相关风格不确定性”（CSU）的新型增强方法，该方法突破了风格统计空间中的线性插值限制，同时保留了关键的相关性信息。我们通过在多种跨域计算机视觉和医学影像分类任务（PACS、Office-Home、Camelyon17数据集及Duke-Market1501实例检索任务）上的大量实验验证了该方法的有效性。结果表明，相较于现有最先进技术，该方法实现了显著的性能提升。源代码已发布于https://github.com/freshman97/CSU。