Conditional entropy minimization principle for learning domain invariant representation features. (arXiv:2201.10460v1 [cs.LG])

Web: http://arxiv.org/abs/2201.10460

Invariance principle-based methods, for example, Invariant Risk Minimization
(IRM), have recently emerged as promising approaches for Domain Generalization
(DG). Despite the promising theory, invariance principle-based approaches fail
in common classification tasks due to the mixture of the true invariant
features and the spurious invariant features. In this paper, we propose a
framework based on the conditional entropy minimization principle to filter out
the spurious invariant features leading to a new algorithm with a better
generalization capability. We theoretically prove that …

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