  Domain generalization (DG) aims to tackle the distribution shift between
training domains and unknown target domains. Generating new domains is one of
the most effective approaches, yet its performance gain depends on the
distribution discrepancy between the generated and target domains.
Distributionally robust optimization is promising to tackle distribution
discrepancy by exploring domains in an uncertainty set. However, the
uncertainty set may be overwhelmingly large, leading to low-confidence
prediction in DG. It is because a large uncertainty set could introduce domains
containing semantically different factors from training domains. To address
this issue, we propose to perform a $\textbf{mo}$derately
$\textbf{d}$istributional $\textbf{e}$xploration (MODE) for domain
generalization. Specifically, MODE performs distribution exploration in an
uncertainty $\textit{subset}$ that shares the same semantic factors with the
training domains. We show that MODE can endow models with provable
generalization performance on unknown target domains. The experimental results
show that MODE achieves competitive performance compared to state-of-the-art
baselines.
