CDC-OIL: Class-Domain Coupled Online Incremental Learning

Real-world data streams often exhibit simultaneous class increment and domain shift, while existing continual learning settings typically study these two sources of non-stationarity in isolation. So we introduce Class-Domain Coupled Online Incremental Learning (CDC-OIL), where class and domain spaces evolve jointly or separately under single-pass data access and ambiguous task boundaries. We further identify a bidirectional interaction between class increment and domain shift. Class increment encourages model to refine semantic features but compress the feature space, increasing sensitivity to domain shift. Conversely, domain shift can enrich cross-domain semantics, but excessive shifts distort class distributions and hinder new-class discrimination. To address this challenge, we propose Class-Domain Decoupled Extensible Gaussian Mixture Models (CDD-EGMMs), which factorize feature distributions into class-specific semantic mixtures and domain shifts shared across classes. This compositional memory supports online updates and generative replay by recombining semantic and domain components without storing raw data. A Gaussian Representation Constraint further promotes class separability as the label space expands. Experiments on four benchmarks demonstrate that CDD-EGMMs consistently improves accuracy and reduces forgetting compared with representative continual learning baselines.