  Adaptation to out-of-distribution data is a meta-challenge for all
statistical learning algorithms that strongly rely on the i.i.d. assumption. It
leads to unavoidable labor costs and confidence crises in realistic
applications. For that, domain generalization aims at mining domain-irrelevant
knowledge from multiple source domains that can generalize to unseen target
domains. In this paper, by leveraging the frequency domain of an image, we
uniquely work with two key observations: (i) the high-frequency information of
an image depicts object edge structure, which preserves high-level semantic
information of the object is naturally consistent across different domains, and
(ii) the low-frequency component retains object smooth structure, while this
information is susceptible to domain shifts. Motivated by the above
observations, we introduce (i) an encoder-decoder structure to disentangle
high- and low-frequency feature of an image, (ii) an information interaction
mechanism to ensure the helpful knowledge from both two parts can cooperate
effectively, and (iii) a novel data augmentation technique that works on the
frequency domain to encourage the robustness of frequency-wise feature
disentangling. The proposed method obtains state-of-the-art performance on
three widely used domain generalization benchmarks (Digit-DG, Office-Home, and
PACS).
