Catastrophic forgetting poses the primary challenge in the continual learning. Nowadays, methods based on parameter-efficient tuning (PET) have demonstrated impressive performance in continual learning. However, these methods are still confronted with a common problem: fine-tuning on consecutive distinct tasks can disrupt the existing parameter distribution and lead to forgetting. Recent progress mainly focused in empirically designing efficient tuning engineering, lacking investigation of forgetting generation mechanism, anti-forgetting criteria and providing theoretical support. Additionally, the unresolved trade-off between learning new content and protecting old knowledge further complicates these challenges. The gradient projection methodology restricts gradient updates to the orthogonal direction of the old feature space, preventing distribution of the parameters from being damaged during updating and significantly suppressing forgetting. Developing on it, in this paper, we reformulate Adapter, LoRA, Prefix, and Prompt to continual learning setting from the perspective of gradient projection, and propose a unified framework called Parameter Efficient Gradient Projection (PEGP). Based on the hypothesis that old tasks should have the same results after model updated, we introduce orthogonal gradient projection into different PET paradigms and theoretically demonstrate that the orthogonal condition for the gradient can effectively resist forgetting in PET-based continual methods. Notably, PEGP is the first unified method to provide an anti-forgetting mechanism with mathematical demonstration for different tuning paradigms. We extensively evaluate our method with different backbones on diverse datasets, and experiments demonstrate its efficiency in reducing forgetting in various incremental settings.

参数高效梯度投影（PEGP）是第一个在不同调节范例中数学证明提供抗遗忘机制的统一方法，通过将正交梯度投影引入不同的PET范例中，从而有效抵制PET基础的连续学习中的遗忘现象。

参数高效连续学习的梯度投影