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: this https URL

我们提出了回忆导向的持续学习框架，平衡稳定性和可塑性的目标，通过分离稳定性和可塑性的机制，在两层架构中，推理网络有效地获取新知识，生成网络在需要时回忆过去的知识，并通过引入生成对抗元模型（GAMM），根据不同表示的知识复杂性来增量学习任务特定参数，通过实验证明，我们的框架不仅能有效地学习新知识而且在任务感知和无任务感知的学习场景中实现了先前知识的高稳定性。