Multivariate Fourier Distribution Perturbation: Domain Shifts with Uncertainty in Frequency Domain

Xianfeng Li, Weijie Chen, Shicai Yang, Yishuang Li, Wenhao Guan, Lin Li

Abstract: Diversifying training data techniques have achieved tremendous success in Domain Generalization (DG) tasks. The key to diversifying domain data is by increasing the types of domain styles. After investigating this issue from the perspective of the Fourier transform, the domain cue is found to be implicitly encoded in the amplitude component of Fourier features, which is more indicative of domain-specific information than statistics (means and standard deviations). However, Fourier-based methods tend to augment amplitude components via linear interpolation between two samples, which limits the diversity. To break this limitation, we aim to augment novel amplitude components from a perturbation perspective, which is termed Multivariate Fourier Distribution Perturbation. Specially, we design channel-wise and pixel-wise random perturbations for in-sample and cross-sample distribution to expand the distribution scope of probabilistic feature amplitude components.