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Found 2 papers, 1 papers with code
Cross-Domain Feature Augmentation for Domain Generalization
Existing methods focus on learning invariance across domains to enhance model robustness, and data augmentation has been widely used to learn invariant predictors, with most methods performing augmentation in the input space.
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization.
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