  Deep neural networks have shown remarkable performance in image
classification. However, their performance significantly deteriorates with
corrupted input data. Domain generalization methods have been proposed to train
robust models against out-of-distribution data. Data augmentation in the
frequency domain is one of such approaches that enable a model to learn phase
features to establish domain-invariant representations. This approach changes
the amplitudes of the input data while preserving the phases. However, using
fixed phases leads to susceptibility to phase fluctuations because amplitudes
and phase fluctuations commonly occur in out-of-distribution. In this study, to
address this problem, we introduce an approach using finite variation of the
phases of input data rather than maintaining fixed phases. Based on the
assumption that the degree of domain-invariant features varies for each phase,
we propose a method to distinguish phases based on this degree. In addition, we
propose a method called vital phase augmentation (VIPAug) that applies the
variation to the phases differently according to the degree of domain-invariant
features of given phases. The model depends more on the vital phases that
contain more domain-invariant features for attaining robustness to amplitude
and phase fluctuations. We present experimental evaluations of our proposed
approach, which exhibited improved performance for both clean and corrupted
data. VIPAug achieved SOTA performance on the benchmark CIFAR-10 and CIFAR-100
datasets, as well as near-SOTA performance on the ImageNet-100 and ImageNet
datasets. Our code is available at https://github.com/excitedkid/vipaug.
