Title: Improving Generalization with Domain Convex Game. Abstract: 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, and the relation between the diversity of source domains and model generalization still remains unclear. We thus made some explorations and found that the model generalization does not strictly improve with the increase of domain diversity, limiting the effectiveness of domain augmentation. In view of this observation, we propose a new perspective on DG that recast it as a convex game between domains.
We formulate a regularization term based on the supermodularity property of convex game which rigorously demonstrates that the growth of domain diversity will enhance model generalization monotonically. This enables model to best utilize the rich information within input data so that each diversified domain contributes to model generalization. Furthermore, we construct a sample filter to eliminate the bad samples which contain unprofitable or even harmful information to generalization performance, such as noisy or redundant samples. Our framework presents a new avenue for the formal analysis of DG, the rationality and effectiveness of which have been demonstrated on extensive benchmark datasets.