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arXiv · 2609.39550

Hyperbolic Prototype Routing for Rehearsal-Free Class-Incremental Learning

HongWei Zhao·Rui Liu·Yong Chen
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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.

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HongWei Zhao, Rui Liu, Yong Chen. 2026-09-30. Hyperbolic Prototype Routing for Rehearsal-Free Class-Incremental Learning. https://arxiv.org/abs/2609.39550

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