Singular Value Fine-tuning for Few-Shot Class-Incremental Learning

Class-Incremental Learning (CIL) aims to prevent catastrophic forgetting of previously learned classes while sequentially incorporating new ones. Few-shot CIL (FSCIL) presents a more challenging setting by introducing novel classes with only a few labeled examples, which significantly increases the risk of overfitting beyond the standard challenges faced in CIL. While catastrophic forgetting has been extensively studied, overfitting in FSCIL, especially when using large foundation models, has received comparatively limited attention. To fill this gap, we propose Singular Value Fine-tuning for FSCIL (SVFCL) and compare it with existing Parameter Efficient Fine-Tuning (PEFT) methods like prompt tuning and Low-Rank Adaptation (LoRA). Specifically, SVFCL applies singular value decomposition to the foundation model weights. For each task, the singular vectors are kept fixed while only the singular values are fine-tuned, and the updated singular values are subsequently merged to obtain the adapted model. This simple yet effective approach not only alleviates the problem of catastrophic forgetting but also mitigates overfitting more effectively by significantly reducing the number of trainable parameters. The effectiveness of SVFCL is validated through extensive experiments on four benchmark datasets, accompanied by thorough ablation studies and in-depth discussions. © 2026 IEEE.