🤖 AI Summary
This study addresses the challenges of cumulative interference-induced catastrophic forgetting and unreliable module matching during inference in class-incremental learning. To this end, it proposes HyPro, a novel framework that assigns independent LoRA experts to individual tasks to achieve isolated representation learning. Furthermore, this work pioneers the projection of routing features onto a Poincaré ball, enabling reliable task-level discrimination through geodesic nearest-prototype matching. By integrating parameter-efficient fine-tuning, a Mixture-of-LoRA-Experts architecture, and hyperbolic geometric computation, the proposed approach demonstrates substantial improvements over existing strong baselines. Extensive experiments on both standard and few-shot class-incremental learning benchmarks confirm that HyPro yields significantly superior average and final accuracy.
📝 Abstract
Class-Incremental Learning (CIL) aims to continually learn new classes while preserving prior knowledge. Parameter-efficient fine-tuning with pre-trained models enables CIL with minimal parameter updates, but existing approaches still suffer from catastrophic forgetting caused by cumulative interference and suboptimal module-sample matching at inference. We propose Hyperbolic Prototype Routing (HyPro), a rehearsal-free framework for continual learning. HyPro allocates a dedicated LoRA-Expert module to each incremental task for isolated representation learning, then projects routing features onto a Poincare ball and performs geodesic nearest-prototype matching for reliable task-level discrimination. Extensive experiments on standard CIL and Few-Shot CIL benchmarks show that HyPro consistently improves average and final-stage accuracy over strong baselines.