  Domain generalization (DG) is a principal task to evaluate the robustness of
computer vision models. Many previous studies have used normalization for DG.
In normalization, statistics and normalized features are regarded as style and
content, respectively. However, it has a content variation problem when
removing style because the boundary between content and style is unclear. This
study addresses this problem from the frequency domain perspective, where
amplitude and phase are considered as style and content, respectively. First,
we verify the quantitative phase variation of normalization through the
mathematical derivation of the Fourier transform formula. Then, based on this,
we propose a novel normalization method, PCNorm, which eliminates style only as
the preserving content through spectral decomposition. Furthermore, we propose
advanced PCNorm variants, CCNorm and SCNorm, which adjust the degrees of
variations in content and style, respectively. Thus, they can learn
domain-agnostic representations for DG. With the normalization methods, we
propose ResNet-variant models, DAC-P and DAC-SC, which are robust to the domain
gap. The proposed models outperform other recent DG methods. The DAC-SC
achieves an average state-of-the-art performance of 65.6% on five datasets:
PACS, VLCS, Office-Home, DomainNet, and TerraIncognita.
