In contrast to the natural capabilities of humans to learn new tasks in a sequential fashion, neural networks are known to suffer from catastrophic forgetting, where the model's performances on old tasks drop dramatically after being optimized for a new task. Since then, the continual learning (CL) community has proposed several solutions aiming to equip the neural network with the ability to learn the current task (plasticity) while still achieving high accuracy on the previous tasks (stability). Despite remarkable improvements, the plasticity-stability trade-off is still far from being solved and its underlying mechanism is poorly understood. In this work, we propose Auxiliary Network Continual Learning (ANCL), a novel method that applies an additional auxiliary network which promotes plasticity to the continually learned model which mainly focuses on stability. More concretely, the proposed framework materializes in a regularizer that naturally interpolates between plasticity and stability, surpassing strong baselines on task incremental and class incremental scenarios. Through extensive analyses on ANCL solutions, we identify some essential principles beneath the stability-plasticity trade-off.

翻译：---- 与人类学习新任务的自然能力不同，神经网络往往会出现灾难性遗忘，即使用新任务优化后，对先前任务的表现急剧下降。因此，持续学习（CL）社区提出了几种解决方案，旨在帮助神经网络在学习新任务的同时仍然保持对旧任务的高准确性。尽管取得了显著进展，但可塑性 - 稳定性权衡仍然远未解决，其基本机制也不太清楚。在这项工作中，我们提出了辅助网络持续学习（ANCL）的新方法，它应用了一个额外的辅助网络来促进持续学习模型的可塑性，而主要关注稳定性。更具体地说，所提出的框架通过自然地插值可塑性和稳定性的正则化，超越了强基线在任务增量和类增量情况下。通过对ANCL的广泛分析，我们确定了稳定性 - 可塑性权衡的一些重要原则。