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.

单域广义化通过在单个源域上训练以提高模型对未知域的泛化能力，然而，有限的训练数据多样性影响了域不变特征的学习，导致泛化性能下降。为了解决这个问题，我们提出了一种名为 CPerb 的简单而有效的跨扰动方法，该方法利用水平和垂直操作来增强训练数据的多样性，从不同角度学习具有相同语义类别样本的多路扰动，并引入一种名为 MixPatch 的新型特征级扰动方法来进一步增加训练数据的多样性。在各种基准数据集上的广泛实验证明了我们方法的有效性。