On Certifying and Improving Generalization to Unseen Domains. (arXiv:2206.12364v1 [cs.LG])

Domain Generalization (DG) aims to learn models whose performance remains
high on unseen domains encountered at test-time by using data from multiple
related source domains. Many existing DG algorithms reduce the divergence
between source distributions in a representation space to potentially align the
unseen domain close to the sources. This is motivated by the analysis that
explains generalization to unseen domains using distributional distance (such
as the Wasserstein distance) to the sources. However, due to the openness of
the DG …

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