The stability-plasticity dilemma is a major challenge in continual learning, as it involves balancing the conflicting objectives of maintaining performance on previous tasks while learning new tasks. In this paper, we propose the recall-oriented continual learning framework to address this challenge. Inspired by the human brain's ability to separate the mechanisms responsible for stability and plasticity, our framework consists of a two-level architecture where an inference network effectively acquires new knowledge and a generative network recalls past knowledge when necessary. In particular, to maximize the stability of past knowledge, we investigate the complexity of knowledge depending on different representations, and thereby introducing generative adversarial meta-model (GAMM) that incrementally learns task-specific parameters instead of input data samples of the task. Through our experiments, we show that our framework not only effectively learns new knowledge without any disruption but also achieves high stability of previous knowledge in both task-aware and task-agnostic learning scenarios. Our code is available at: https://github.com/bigdata-inha/recall-oriented-cl-framework.

翻译：稳定性-可塑性困境是持续学习中的主要挑战，它涉及在保持以往任务性能与学习新任务这一相互冲突的目标之间取得平衡。本文提出面向回忆的持续学习框架以应对该挑战。受人类大脑能够分离负责稳定性与可塑性的机制启发，该框架采用双层架构：推理网络有效获取新知识，生成网络在必要时回忆过往知识。具体而言，为最大化过往知识的稳定性，我们基于不同表征探究知识的复杂性，从而引入生成对抗元模型（GAMM），该模型增量式学习任务特定参数而非任务输入数据样本。实验表明，该框架不仅能无干扰地有效学习新知识，而且在任务感知与任务无关两种学习场景下均能保持既往知识的高度稳定性。我们的代码可在 https://github.com/bigdata-inha/recall-oriented-cl-framework 获取。