Cross-Domain Feature Augmentation for Domain Generalization
This work addresses the problem of model robustness to distribution shifts for machine learning practitioners, representing an incremental improvement over existing feature augmentation methods.
The paper tackles domain generalization by proposing a cross-domain feature augmentation method, XDomainMix, which decomposes features into components to increase sample diversity and learn invariant representations, achieving state-of-the-art performance on benchmark datasets.
Domain generalization aims to develop models that are robust to distribution shifts. 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. However, augmentation in the input space has limited diversity whereas in the feature space is more versatile and has shown promising results. Nonetheless, feature semantics is seldom considered and existing feature augmentation methods suffer from a limited variety of augmented features. We decompose features into class-generic, class-specific, domain-generic, and domain-specific components. We propose a cross-domain feature augmentation method named XDomainMix that enables us to increase sample diversity while emphasizing the learning of invariant representations to achieve domain generalization. Experiments on widely used benchmark datasets demonstrate that our proposed method is able to achieve state-of-the-art performance. Quantitative analysis indicates that our feature augmentation approach facilitates the learning of effective models that are invariant across different domains.