Domain Generalization Guided by Class-wise Domain-Invariant Semantic Subspaces

Domain generalization aims to learn a model from multiple source domains that generalizes to unseen target domains. Existing domain generalization methods typically model each class as a single prototype vector, that is, a point in Euclidean space, making it difficult to explicitly characterize intra-class variation under domain shift at the class level. To this end, we model each class as a semantic subspace. Specifically, we use multiple class-related prompts to estimate the class semantic center and its principal variation directions, extending the point representation to a low-dimensional affine subspace. The model combines prototype similarity with subspace projection similarity, thereby providing semantic discriminative evidence beyond a single-point prototype for samples of the same class across different domains. To preserve the semantic structure of the subspace and improve prediction stability against variations not represented by it, we combine semantic geometry constraints with residual-space robustness learning. Theoretical analysis characterizes the representational properties of the subspace and prediction stability under bounded residual perturbations. Experiments on multiple domain generalization benchmarks show that our method achieves stable and competitive performance.