  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.
