  Domain generalization is the task of learning models that generalize to
unseen target domains. We propose a simple yet effective method for domain
generalization, named cross-domain ensemble distillation (XDED), that learns
domain-invariant features while encouraging the model to converge to flat
minima, which recently turned out to be a sufficient condition for domain
generalization. To this end, our method generates an ensemble of the output
logits from training data with the same label but from different domains and
then penalizes each output for the mismatch with the ensemble. Also, we present
a de-stylization technique that standardizes features to encourage the model to
produce style-consistent predictions even in an arbitrary target domain. Our
method greatly improves generalization capability in public benchmarks for
cross-domain image classification, cross-dataset person re-ID, and
cross-dataset semantic segmentation. Moreover, we show that models learned by
our method are robust against adversarial attacks and image corruptions.
