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.

本文提出了一种名为 XDED 的跨域集合蒸馏方法，通过学习域不变特征并鼓励模型收敛于平坦极小值来提高泛化能力，同时提出了解决任意目标域中样式连贯性的去风格化方法。该方法在交叉领域图像分类、跨数据集的 person re-ID 和语义分割中极大地提高了泛化能力，并且表明所学习的模型对抗攻击和图像破坏是鲁棒的。