Continual learning is a learning paradigm that learns tasks sequentially with resources constraints, in which the key challenge is stability-plasticity dilemma, i.e., it is uneasy to simultaneously have the stability to prevent catastrophic forgetting of old tasks and the plasticity to learn new tasks well. In this paper, we propose a new continual learning approach, Advanced Null Space (AdNS), to balance the stability and plasticity without storing any old data of previous tasks. Specifically, to obtain better stability, AdNS makes use of low-rank approximation to obtain a novel null space and projects the gradient onto the null space to prevent the interference on the past tasks. To control the generation of the null space, we introduce a non-uniform constraint strength to further reduce forgetting. Furthermore, we present a simple but effective method, intra-task distillation, to improve the performance of the current task. Finally, we theoretically find that null space plays a key role in plasticity and stability, respectively. Experimental results show that the proposed method can achieve better performance compared to state-of-the-art continual learning approaches.

本文提出了一种新的连续学习方法Advanced Null Space (AdNS)，通过使用低秩逼近获取新的零空间并将梯度投影到零空间中，从而实现平衡稳定性和可塑性，解决了连续学习中的稳定性-可塑性困境，并提出了一种简单而有效的方法intra-task distillation来提高当前任务的性能。实验结果表明，与最先进的连续学习方法相比，该方法可以实现更好的性能。

通过高级零空间在不间断学习中平衡稳定性与可塑性