Geo-LoRA: Geometry-Aware Subspace Evolution for Low-Rank Adaptation in Continual Learning
Rehearsal-free class-incremental learning (CIL) with LoRA adapters remains challenging because the low-rank subspaces updated across tasks evolve without geometric control, causing unstable shared representations and repetitive collapse of task-specific updates into previously occupied directions. We introduce Geo-LoRA, a geometry-aware framework that explicitly regulates how low-rank subspaces, both shared and task-specific, evolve during continual learning. For the shared branch, Subspace Projection Preservation (SPP) constrains consecutive updates to follow smooth trajectories on the Grassmann manifold, and Adaptive Core-Slack Alignment (ACSA) decomposes transitions into principal and ...
| Authors | Yibo Feng |
|---|---|
| Primary category | cs.CV |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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