引入因果发现学习的跨领域知识泛化方法

Domain generalization aims to generalize knowledge from multiple known domains to unknown target domains. However, existing models are easily affected by high-dimensional noise when extracting image features, which causes the unstable relationship between the extracted image features and labels. Thus, inspired by the cross-domain invariant causal mechanism, we propose a cross-domain knowledge generalization method introducing causal discovery learning. Specifically, we extract the low-dimensional latent features of the image to retain the basic information of the image. Meanwhile, we perform variational inference on the low-dimensional latent features to achieve mutual independence of latent feature variables. We reconstruct the causal directed acyclic graphs (DAG) between latent feature variables and category labels to discover the latent feature variables that have stable causal structures with category labels. We introduce a counterfactual contrastive regularization term, which exploits counterfactual variance and invariance during data generation to make causal inference and generate causal invariant representations. To verify the proposed method, we conducted tests on five datasets under the DomainBed framework and four datasets under the SWAD framework. Experiments show that compared with existing methods, our domain generalization model has greater improvements in performance and adaptability.