LoRA-Based Continual Learning with Constraints on Critical Parameter Changes
This work addresses forgetting in continual learning for AI systems, but it is incremental as it builds on existing orthogonal LoRA tuning methods.
The paper tackles the problem of critical parameter changes in LoRA-based continual learning by freezing key Vision Transformer matrices and introducing orthogonal LoRA composition, achieving state-of-the-art results with a 6.35% accuracy improvement and 3.24% reduction in forgetting on Split CIFAR-100.
LoRA-based continual learning represents a promising avenue for leveraging pre-trained models in downstream continual learning tasks. Recent studies have shown that orthogonal LoRA tuning effectively mitigates forgetting. However, this work unveils that under orthogonal LoRA tuning, the critical parameters for pre-tasks still change notably after learning post-tasks. To address this problem, we directly propose freezing the most critical parameter matrices in the Vision Transformer (ViT) for pre-tasks before learning post-tasks. In addition, building on orthogonal LoRA tuning, we propose orthogonal LoRA composition (LoRAC) based on QR decomposition, which may further enhance the plasticity of our method. Elaborate ablation studies and extensive comparisons demonstrate the effectiveness of our proposed method. Our results indicate that our method achieves state-of-the-art (SOTA) performance on several well-known continual learning benchmarks. For instance, on the Split CIFAR-100 dataset, our method shows a 6.35\% improvement in accuracy and a 3.24\% reduction in forgetting compared to previous methods. Our code is available at https://github.com/learninginvision/LoRAC-IPC.