Machine learning (ML) robustness and domain generalization are fundamentally correlated: they essentially concern data distribution shifts under adversarial and natural settings, respectively. On one hand, recent studies show that more robust (adversarially trained) models are more generalizable. On the other hand, there is a lack of theoretical understanding of their fundamental connections. In this paper, we explore the relationship between regularization and domain transferability considering different factors such as norm regularization and data augmentations (DA). We propose a general theoretical framework proving that factors involving the model function class regularization are sufficient conditions for relative domain transferability. Our analysis implies that "robustness" is neither necessary nor sufficient for transferability; rather, robustness induced by adversarial training is a by-product of such function class regularization. We then discuss popular DA protocols and show when they can be viewed as the function class regularization under certain conditions and therefore improve generalization. We conduct extensive experiments to verify our theoretical findings and show several counterexamples where robustness and generalization are negatively correlated on different datasets.

本文研究机器学习中，鲁棒性和域泛化的联系，提出基于函数类正则化的域转移的充分条件，证明了鲁棒性对域泛化不是必要也不充分的，数据增强和正则化可以提高泛化性能。

对抗鲁棒模型未必具备更好的迁移能力: 从正则化角度出发得到域迁移的充分条件