A Causal Framework to Unify Common Domain Generalization Approaches
Nevin L. Zhang Kaican Li Han Gao Weiyan Xie Zhi Lin Zhenguo Li Luning Wang Yongxiang Huang
Domain generalization (DG) is about learning models that generalize well to
new domains that are related to, but different from, the training domain(s). It
is a fundamental problem in machine learning and has attracted much attention
in recent years. A large number of approaches have been proposed. Different
approaches are motivated from different perspectives, making it difficult to
gain an overall understanding of the area. In this paper, we propose a causal
framework for domain generalization and present an understanding of common DG
approaches in the framework. Our work sheds new lights on the following
questions: (1) What are the key ideas behind each DG method? (2) Why is it
expected to improve generalization to new domains theoretically? (3) How are
different DG methods related to each other and what are relative advantages and
limitations? By providing a unified perspective on DG, we hope to help
researchers better understand the underlying principles and develop more
effective approaches for this critical problem in machine learning.