Equivariant Disentangled Transformation for Domain Generalization under Combination Shift

Yivan Zhang, Jindong Wang, Xing Xie, Masashi Sugiyama

Keywords: distribution shift, domain generalization, equivariance, invariance, disentanglement, algebraic theory, group theory, category theory

TL;DR: Learning data augmentations based on the algebraic structures of labels is a promising approach for combination shift.

Abstract: Machine learning systems may encounter unexpected problems when the data distribution changes in the deployment environment. A major reason is that certain combinations of domains and labels are not observed during training but appear in the test environment. Although various invariance-based algorithms can be applied, we find that the performance gain is often marginal. To formally analyze this issue, we provide a unique algebraic formulation of the combination shift problem based on the concepts of homomorphism, equivariance, and a refined definition of disentanglement. The algebraic requirements naturally derive a simple yet effective method, referred to as equivariant disentangled transformation (EDT), which augments the data based on the algebraic structures of labels and makes the transformation satisfy the equivariance and disentanglement requirements. Experimental results demonstrate that invariance may be insufficient, and it is important to exploit the equivariance structure in the combination shift problem.

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