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