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

Domain Generalization with Correlated Style Uncertainty