  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.
