Single-source Domain Generalization (SDG) is a promising yet challenging technology that aims to transfer knowledge from a singular source domain to multiple and unfamiliar target domains. Existing SDG methods typically rely on domain expansion to implement data variation and broaden the coverage of the training domain. However, due to the lack of proper semantic consistency and sample diversity constraints, these methods have limited improvement in generalization performance for most practical applications. In this paper, we propose a Causality-Aware Single-source Domain Generalization (CASDG) method to utilize both semantic consistency and diversity during the data transformation process. First, a causality-aware module is designed to accurately measure the causal effect between latent features and labels. Then, we introduce a causal domain expansion module, which utilizes the causal effect matrix as a semantic consistency constraint and mutual information as a sample diversity constraint. These two constraints are jointly used to encourage the style transformer to generate new auxiliary samples that are undeviated from the original samples. The image classification model using our method can produce the best classification performance for unknown domain data compared to the state-of-the-art methods.

Learning domain-invariant feature representations is critical to alleviate the distribution differences between training and testing domains. The existing mainstream domain generalization approaches primarily pursue to align the across-domain distributions to extract the transferable feature representations. However, these representations may be insufficient and unstable. Moreover, these networks may also undergo catastrophic forgetting because the previous learned knowledge is replaced by the new learned knowledge. To cope with these issues, we propose a novel causality-based contrastive incremental learning model for domain generalization, which mainly includes three components: (1) intra-domain causal factorization, (2) inter-domain Mahalanobis similarity metric, and (3) contrastive knowledge distillation. The model extracts intra and inter domain-invariant knowledge to improve model generalization. Specifically, we first introduce a causal factorization to extract intra-domain invariant knowledge. Then, we design a Mahalanobis similarity metric to extract common inter-domain invariant knowledge. Finally, we propose a contrastive knowledge distillation with exponential moving average to distill model parameters in a smooth way to preserve the previous learned knowledge and mitigate model forgetting. Extensive experiments on several domain generalization benchmarks prove that our model achieves the state-of-the-art results, which sufficiently show the effectiveness of our model.