LoRA-Based Continual Learning with Constraints on Critical Parameter Changes

Shimou Ling a, Liang Zhang a, Jiangwei Zhao a, Lili Pan a,, Hongliang Li a a University of Electronic Science and T echnology of China, Chengdu, ChinaAbstract 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 e ffectively 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 e ffectiveness of our proposed method. Our results indicate that our method achieves state-of-the-art (SOT A) 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. Introduction Continual learning (CL) is the process of sequentially training a model on multiple tasks while retaining knowledge acquired from previous tasks [1, 2]. Neural networks often forget knowledge learned from previous tasks after acquiring new knowledge, a phenomenon known as catastrophic forgetting [3]. Significant e fforts have been made to alleviate catastrophic forgetting in neural networks in recent years. These studies can be categorized into three main approaches: architecture-based [4, 5, 6, 7], regularization-based [8, 9, 10, 11], and replay-based [12, 13, 14, 15]. Despite their proven eff ectiveness, these approaches still fall short of practical requirements. Over the past year, visual continual learning combined with pre-trained models (PTMs) has demonstrated significant superiority in alleviating forgetting. Prompt tuning has become the most common method to integrate PTMs with continual learning.