DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning

TL;DR

DESIRE is a rehearsal-free continual learning method that uses LoRA-based modules and post-processing techniques to effectively retain old knowledge and adapt to new tasks without catastrophic forgetting.

Contribution

The paper introduces DESIRE, a novel LoRA-based rehearsal-free approach with dynamic consolidation and boundary refinement for improved continual learning performance.

Findings

Achieves state-of-the-art results on multiple datasets.

Effectively balances stability and plasticity.

Reduces catastrophic forgetting without rehearsal.

Abstract

Continual learning aims to equip models with the ability to retain previously learned knowledge like a human. Recent work incorporating Parameter-Efficient Fine-Tuning has revitalized the field by introducing lightweight extension modules. However, existing methods usually overlook the issue of information leakage caused by the fact that the experiment data have been used in pre-trained models. Once these duplicate data are removed in the pre-training phase, their performance can be severely affected. In this paper, we propose a new LoRA-based rehearsal-free method named DESIRE. Our method avoids imposing additional constraints during training to mitigate catastrophic forgetting, thereby maximizing the learning of new classes. To integrate knowledge from old and new tasks, we propose two efficient post-processing modules. On the one hand, we retain only two sets of LoRA parameters for merging and propose dynamic representation consolidation to calibrate the merged feature representation. On the other hand, we propose decision boundary refinement to address classifier bias when training solely on new class data. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple datasets and strikes an effective balance between stability and plasticity. Our code will be publicly available.