  Modern deep neural networks suffer from performance degradation when
evaluated on testing data under different distributions from training data.
Domain generalization aims at tackling this problem by learning transferable
knowledge from multiple source domains in order to generalize to unseen target
domains. This paper introduces a novel Fourier-based perspective for domain
generalization. The main assumption is that the Fourier phase information
contains high-level semantics and is not easily affected by domain shifts. To
force the model to capture phase information, we develop a novel Fourier-based
data augmentation strategy called amplitude mix which linearly interpolates
between the amplitude spectrums of two images. A dual-formed consistency loss
called co-teacher regularization is further introduced between the predictions
induced from original and augmented images. Extensive experiments on three
benchmarks have demonstrated that the proposed method is able to achieve
state-of-the-arts performance for domain generalization.
