Equivariant Disentangled Transformation for Domain Generalization under Combination Shift

@article{Zhang2022EquivariantDT,
  title={Equivariant Disentangled Transformation for Domain Generalization under Combination Shift},
  author={Yivan Zhang and Jindong Wang and Xingxu Xie and Masashi Sugiyama},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.02011}
}
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… 

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