  Deep neural networks (DNNs) have shown exciting performance in various tasks,
yet suffer generalization failures when meeting unknown target domains. One of
the most promising approaches to achieve domain generalization (DG) is
generating unseen data, e.g., mixup, to cover the unknown target data. However,
existing works overlook the challenges induced by the simultaneous appearance
of changes in both the semantic and distribution space. Accordingly, such a
challenge makes source distributions hard to fit for DNNs. To mitigate the
hard-fitting issue, we propose to perform a semantic-aware mixup (SAM) for
domain generalization, where whether to perform mixup depends on the semantic
and domain information. The feasibility of SAM shares the same spirits with the
Fourier-based mixup. Namely, the Fourier phase spectrum is expected to contain
semantics information (relating to labels), while the Fourier amplitude retains
other information (relating to style information). Built upon the insight, SAM
applies different mixup strategies to the Fourier phase spectrum and amplitude
information. For instance, SAM merely performs mixup on the amplitude spectrum
when both the semantic and domain information changes. Consequently, the
overwhelmingly large change can be avoided. We validate the effectiveness of
SAM using image classification tasks on several DG benchmarks.
