CoDC: Unified Diffusion and Classification for Enhanced Class-Incremental Learning

In class-incremental learning (CIL), a model must learn new classes while retaining previous knowledge without storing all historical data. Generative replay mitigates catastrophic forgetting by synthesizing old class samples, but conventional pipelines usually train separate generative and classification models and can be degraded by generated images of poor quality. This paper proposes the Co-Diffusion Classifier (CoDC), a unified framework based on diffusion that performs image generation and classification in a single network. CoDC attaches a classification branch to the UNet encoder and introduces an exponential noise filtering loss according to diffusion timesteps so that cleaner samples contribute more strongly to representation learning. A base task classification pre-training stage followed by collaborative training with selective parameter freezing reduces conflicts between noise prediction and semantic feature extraction. For rehearsal-free replay, generated samples are selected using confidence and feature consistency filters. Experiments on CIFAR-100, FaceScrub, and Flowers-102 show that CoDC maintains strong incremental accuracy without storing real old class exemplars. Additional comparisons that account for protocol differences with recent exemplar-free and pre-trained model methods clarify the setting in which CoDC is most directly comparable.