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Domain generalization is challenging due to the domain shift and theuncertainty caused by the inaccessibility of target domain data. In this paper,we address both challenges with a probabilistic framework based on variationalBayesian inference, by incorporating uncertainty into neural network weights.We couple domain invariance in a probabilistic formula with the variationalBayesian inference. This enables us to explore domain-invariant learning in aprincipled way. Specifically, we derive domain-invariant representations andclassifiers, which are jointly established in a two-layer Bayesian neuralnetwork. We empirically demonstrate the effectiveness of our proposal on fourwidely used cross-domain visual recognition benchmarks. Ablation studiesvalidate the synergistic benefits of our Bayesian treatment when jointlylearning domain-invariant representations and classifiers for domaingeneralization. Further, our method consistently delivers state-of-the-art meanaccuracy on all benchmarks.

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty