Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental Learning

Class-incremental learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. Recent advances in parameter-efficient fine-tuning (PEFT) based on pre-trained models (PTMs) have shown promise in this setting by integrating new tasks with minimal parameter overhead. However, these methods often suffer from knowledge degradationdue to: (1) cumulative interference caused by iterative updates, constrained gradient flows, or entangled module integration; and (2) suboptimal alignment between inference samples and specialized modules. To address these challenges, we propose Dynamic LoRA-Experts and Prototype-Ensemble Matching (DLEPEM), a novel two-stage, rehearsal-free framework. In the first stage, we allocate a task-specific LoRA-Expert for each incremental task, enabling isolated representation learning and reducing cross-task interference. In the second stage, we introduce a prototype-ensemble-matching mechanism that combines general prototypes derived from the frozen PTM with task-adaptive prototypes learned by the LoRA-Experts. This fusion facilitates both strong generalization and precise task-level discrimination. Extensive experiments on standard CIL and few-shot class-incremental learning (FSCIL) benchmarks demonstrate that DLEPEM achieves strong performance under the evaluated protocols. For instance, in CIL, it achieves 93.39% on CIFAR100 (+0.80% over EASE), 92.31% on CUB200 (+2.11% over EASE), and 91.84% on VTAB (+1.39% over EASE). In the more challenging FSCIL setting, it achieves 88.77% on CUB200, outperforming the strongest baseline by a clear margin of 5.31%. These results indicate that DLEPEM effectively mitigates catastrophic forgetting while enhancing incremental learning capability.