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：与人类能够按顺序学习新任务的自然能力不同，神经网络已知会遭受灾难性遗忘——即模型在针对新任务优化后，其在旧任务上的性能会急剧下降。为此，持续学习领域提出了多种解决方案，旨在赋予神经网络学习当前任务的能力（可塑性），同时仍能保持对先前任务的高准确率（稳定性）。尽管取得了显著进展，但稳定性-可塑性的权衡问题仍远未解决，其背后的机制也尚未被充分理解。在本工作中，我们提出了一种新颖方法——辅助网络持续学习，该方法通过引入一个额外的辅助网络来增强主模型的可塑性，而主模型主要关注稳定性。具体而言，所提出的框架通过一个正则化器实现，该正则化器自然地在可塑性与稳定性之间进行插值，在任务增量学习和类增量学习场景中均超越了强基线方法。通过对ANCL解的广泛分析，我们揭示了稳定性-可塑性权衡背后的一些基本原理。