作者：Fangrui Lv Jian Liang Shuang Li Jinming Zhang Di Liu
领域泛化（DG）倾向于通过多个源领域的学习模型来缓解深度神经网络泛化能力差的问题。DG的一个经典解决方案是域扩充，其普遍观点是使源域多样化将有助于分布外的泛化。然而，这些主张是凭直觉理解的，而不是从数学上理解的。我们的探索实证表明，模型泛化和领域多样性之间的相关性可能不是严格正的，这限制了领域扩充的有效性。因此，这项工作旨在保证并进一步提高这一环节的有效性。为此，我们提出了一个关于DG的新视角，将其重塑为域之间的凸博弈。我们首先鼓励每个多样化的领域通过精心设计一个基于超模的正则化项来增强模型泛化。同时，构造了一个样本滤波器来消除低质量
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.
论文链接：http://arxiv.org/pdf/2303.13297v1
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