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.

翻译：单域泛化旨在提升模型在仅依赖单一源域训练时对未知域的泛化能力。然而，训练数据多样性的不足阻碍了域不变特征的学习，导致泛化性能受损。为此，数据扰动（扩充）已成为增加数据多样性的关键方法。但现有扰动方法往往独立关注图像级或特征级扰动，忽视了二者的协同效应。为克服这些局限，我们提出CPerb——一种简单高效的交叉扰动方法。具体而言，CPerb同时运用水平与垂直操作：在水平方向上，通过图像级与特征级扰动增强训练数据多样性，缓解单源域数据多样性不足的问题；在垂直方向上，引入多路径扰动机制，从同一语义类别的样本不同视角学习域不变特征，从而提升模型泛化能力。此外，我们提出MixPatch——一种利用局部图像风格信息进一步丰富训练数据的新型特征级扰动方法。在多个基准数据集上的大量实验验证了本方法的有效性。