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Learning Domain-Invariant Relationship with Instrumental Variable for Domain Generalization
[article]
2021
arXiv
pre-print
Domain generalization (DG) aims to learn from multiple source domains a model that generalizes well on unseen target domains. Existing methods mainly learn input feature representations with invariant marginal distribution, while the invariance of the conditional distribution is more essential for unknown domain generalization. This paper proposes an instrumental variable-based approach to learn the domain-invariant relationship between input features and labels contained in the conditional
arXiv:2110.01438v1
fatcat:uwvoxpo66jb7hccl7er4dalvp4