Domain generalization (DG) tends to alleviate the poor generalization capability of deep neural networks by learning model with multiple source domains. A classical solution to DG is domain augmentation, the common belief of which is that diversifying source domains will be conducive to the out-of-distribution generalization. However, these claims are understood intuitively, rather than mathematically. Our explorations empirically reveal that the correlation between model generalization and the diversity of domains may be not strictly positive, which limits the effectiveness of domain augmentation. This work therefore aim to guarantee and further enhance the validity of this strand. To this end, we propose a new perspective on DG that recasts it as a convex game between domains. We first encourage each diversified domain to enhance model generalization by elaborately designing a regularization term based on supermodularity. Meanwhile, a sample filter is constructed to eliminate low-quality samples, thereby avoiding the impact of potentially harmful information. Our framework presents a new avenue for the formal analysis of DG, heuristic analysis and extensive experiments demonstrate the rationality and effectiveness.

翻译：域泛化旨在通过利用多个源域学习模型，缓解深度神经网络泛化能力不足的问题。域泛化的经典解决方案是域增强，其普遍观点认为，丰富源域的多样性将有利于分布外泛化。然而，这些主张仅停留在直觉认知层面，缺乏数学层面的严谨支撑。我们的实证研究揭示：模型泛化能力与域多样性之间可能并非严格正相关，这限制了域增强的有效性。因此，本研究致力于保证并进一步增强该方法的有效性。为此，我们提出了一种全新视角，将域泛化重构为域间的凸博弈。首先，我们基于超模性精心设计正则化项，促使每个多样化域增强模型泛化能力；同时，构建样本过滤器剔除低质量样本，以避免潜在有害信息的影响。该框架为域泛化的形式化分析开辟了新路径，启发式分析与大量实验验证了其合理性与有效性。