A Novel Cross-Perturbation for Single Domain Generalization
Dongjia Zhao Lei Qi Xiao Shi Yinghuan Shi Xin Geng
Single domain generalization aims to enhance the ability of the model to
generalize to unknown domains when trained on a single source domain. However,
the limited diversity in the training data hampers the learning of
domain-invariant features, resulting in compromised generalization performance.
To address this, data perturbation (augmentation) has emerged as a crucial
method to increase data diversity. Nevertheless, existing perturbation methods
often focus on either image-level or feature-level perturbations independently,
neglecting their synergistic effects. To overcome these limitations, we propose
CPerb, a simple yet effective cross-perturbation method. Specifically, CPerb
utilizes both horizontal and vertical operations. Horizontally, it applies
image-level and feature-level perturbations to enhance the diversity of the
training data, mitigating the issue of limited diversity in single-source
domains. Vertically, it introduces multi-route perturbation to learn
domain-invariant features from different perspectives of samples with the same
semantic category, thereby enhancing the generalization capability of the
model. Additionally, we propose MixPatch, a novel feature-level perturbation
method that exploits local image style information to further diversify the
training data. Extensive experiments on various benchmark datasets validate the
effectiveness of our method.