Unbiased Semantic Representation Learning Based on Causal Disentanglement for Domain Generalization

Abstract

Domain generalization primarily mitigates domain shift among multiple source domains, generalizing the trained model to an unseen target domain. However, the spurious correlation usually caused by context prior (e.g., background) makes it challenging to get rid of the domain shift. Therefore, it is critical to model the intrinsic causal mechanism. The existing domain generalization methods only attend to disentangle the semantic and context-related features by modeling the causation between input and labels, which totally ignores the unidentifiable but important confounders. In this article, a Causal Disentangled Intervention Model (CDIM) is proposed for the first time, to the best of our knowledge, to construct confounders via causal intervention. Specifically, a generative model is employed to disentangle the semantic and context-related features. The contextual information of each domain from generative model can be considered as a confounder layer, and the center of all context-related features is utilized for fine-grained hierarchical modeling of confounders. Then the semantic and confounding features from each layer are combined to train an unbiased classifier, which exhibits both transferability and robustness across an unknown distribution domain. CDIM is evaluated on three widely recognized benchmark datasets, namely, Digit-DG, PACS, and NICO, through extensive ablation studies. The experimental results clearly demonstrate that the proposed model achieves state-of-the-art performance.