  The topic of generalizing machine learning models learned on a collection of
source domains to unknown target domains is challenging. While many domain
generalization (DG) methods have achieved promising results, they primarily
rely on the source domains at train-time without manipulating the target
domains at test-time. Thus, it is still possible that those methods can overfit
to source domains and perform poorly on target domains. Driven by the
observation that domains are strongly related to styles, we argue that reducing
the gap between source and target styles can boost models' generalizability. To
solve the dilemma of having no access to the target domain during training, we
introduce Test-time Fourier Style Calibration (TF-Cal) for calibrating the
target domain style on the fly during testing. To access styles, we utilize
Fourier transformation to decompose features into amplitude (style) features
and phase (semantic) features. Furthermore, we present an effective technique
to Augment Amplitude Features (AAF) to complement TF-Cal. Extensive experiments
on several popular DG benchmarks and a segmentation dataset for medical images
demonstrate that our method outperforms state-of-the-art methods.
