Causality-Aware Single-Source Domain Generalization for Transferring Knowledge to Multiple, Unknown Target Domain

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.