CVPR 2023 – Improving Generalization with Domain Convex Game

In this episode we discuss Improving Generalization with Domain Convex Game
by Fangrui Lv, Jian Liang, Shuang Li, Jinming Zhang, Di Liu. The paper explores the effectiveness of domain augmentation in domain generalization. The authors propose a new perspective on DG as a convex game between domains and design a regularization term based on supermodularity to enhance model generalization for each diversified domain. They also construct a sample filter to eliminate low-quality samples to avoid potentially harmful information. The framework presented in the paper provides a new avenue for the formal analysis of DG, which is supported by heuristic analysis and extensive experiments.