Enhancing decision boundaries in continual learning through a decoupled Gaussian framework

The goal of continual learning (CL) is to acquire new knowledge while retaining previously learned information. CNN-based and prompt-based CL methods have achieved remarkable progress in recent years. However, most prior work has primarily focused on reducing forgetting from the perspective of the model itself. In this paper, we investigate CL from the perspective of decision boundaries, analyzing the impact of instance-level feature overlap. To address this issue, we propose a generic Decoupled Gaussian Softmax Classifier that enhances class discriminability during CL process. Specifically, we decouple the features extracted by the backbone into multiple Gaussian distributions, which are directly fused into the feature space through weighted integration. A regularization term is introduced to penalize the overlap of similar features, while an adaptive decision boundary is assigned to each class to encourage inter-class separation and intra-class compactness. Experiments on 4 widely used continual learning datasets and 12 CL scenarios show that our method has good plug-and-play capability. It improves the average accuracy by 1%–2.63% over the baseline models, while effectively reducing both the forgetting rate and the Expected Calibration Error. Our code is available at: https://anonymous.4open.science/r/DGSC-main-310D.