Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization

Understand

Domain Generalization (DG) is a fundamental challenge for machine learning models, which aims to improve model generalization on various domains.

• Previous methods focus on generating domain invariant features from various source domains.
• However, we argue that the domain variantions also contain useful information, ie, classification-aware information, for downstream tasks, which has been largely ignored.
• Different from learning domain invariant features from source domains, we decouple the input images into Domain Expert Features and noise.