  Domain generalization models learn to generalize to previously unseen
domains, but suffer from prediction uncertainty and domain shift. In this
paper, we address both problems. We introduce a probabilistic meta-learning
model for domain generalization, in which classifier parameters shared across
domains are modeled as distributions. This enables better handling of
prediction uncertainty on unseen domains. To deal with domain shift, we learn
domain-invariant representations by the proposed principle of meta variational
information bottleneck, we call MetaVIB. MetaVIB is derived from novel
variational bounds of mutual information, by leveraging the meta-learning
setting of domain generalization. Through episodic training, MetaVIB learns to
gradually narrow domain gaps to establish domain-invariant representations,
while simultaneously maximizing prediction accuracy. We conduct experiments on
three benchmarks for cross-domain visual recognition. Comprehensive ablation
studies validate the benefits of MetaVIB for domain generalization. The
comparison results demonstrate our method outperforms previous approaches
consistently.
