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.

领域泛化通过提高模型的鲁棒性来开发具有分布偏移上的稳定性的模型。现有方法在跨域数据上学习不变性以增强模型的稳健性，数据增强被广泛用于学习不变的预测模型，其中大多数方法在输入空间进行增强。然而，输入空间的增强的多样性有限，而特征空间的增强更加灵活且显示了有希望的结果。然而，特征语义很少被考虑，现有特征增强方法缺乏增强特征的多样性。我们将特征分解为类通用、类特定、领域通用和领域特定组件。我们提出了一种名为 XDomainMix 的跨域特征增强方法，使我们能够增加样本的多样性，同时强调学习不变表示以实现领域泛化。对广泛使用的基准数据集的实验证明我们的方法能够实现最先进的性能。定量分析表明我们的特征增强方法有助于学习在不同领域中都不变的有效模型。