Cross-Domain Ensemble Distillation for Domain Generalization

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.