Domain generalization aims to develop models that are robust to distribution shifts. Existing methods focus on learning invariance across domains to enhance model robustness, and data augmentation has been widely used to learn invariant predictors, with most methods performing augmentation in the input space. However, augmentation in the input space has limited diversity whereas in the feature space is more versatile and has shown promising results. Nonetheless, feature semantics is seldom considered and existing feature augmentation methods suffer from a limited variety of augmented features. We decompose features into class-generic, class-specific, domain-generic, and domain-specific components. We propose a cross-domain feature augmentation method named XDomainMix that enables us to increase sample diversity while emphasizing the learning of invariant representations to achieve domain generalization. Experiments on widely used benchmark datasets demonstrate that our proposed method is able to achieve state-of-the-art performance. Quantitative analysis indicates that our feature augmentation approach facilitates the learning of effective models that are invariant across different domains.

翻译：领域泛化旨在开发对分布偏移鲁棒的模型。现有方法侧重于学习跨域不变性以增强模型鲁棒性，数据增强被广泛用于学习不变预测器，多数方法在输入空间进行增强。然而，输入空间的增强多样性有限，而特征空间的增强更具灵活性且已展现出良好效果。但特征语义学鲜少被考虑，现有特征增强方法存在增强特征种类匮乏的问题。我们将特征分解为类通用、类特定、域通用和域特定四个分量，提出名为XDomainMix的跨域特征增强方法，可在提升样本多样性的同时强化不变表示学习以实现领域泛化。在广泛使用的基准数据集上的实验表明，所提方法能够达到最先进性能。定量分析显示，我们的特征增强方法有助于学习跨域不变的有效模型。