In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Experience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. We shed new light on this question by showing that applying ER causes the newly added classes' representations to overlap significantly with the previous classes, leading to highly disruptive parameter updates. Based on this empirical analysis, we propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes. We show that using an asymmetric update rule pushes new classes to adapt to the older ones (rather than the reverse), which is more effective especially at task boundaries, where much of the forgetting typically occurs. Empirical results show significant gains over strong baselines on standard continual learning benchmarks

翻译：在在线持续学习模式中,代理商必须在尊重记忆和计算限制的同时从不断变化的分布中学习,同时尊重记忆和计算限制。经验回放(ER)是一个简单而有效的学习战略,在新数据的同时储存和重放少量过去的数据,经验回放(ER)已经成为一种简单有效的学习战略。在这项工作中,我们注重的是,在接收的数据流中出现先前未观测到的类别时,观察到的数据的表示方式的变化,新类别必须与以往的类别区分开来。我们通过显示应用ER导致新增加的类别表示方式与前一个类别严重重叠,导致极具破坏性的参数更新。根据这一经验分析,我们提出了一种新的方法,通过保护学到的表述方式,避免适应新类别,避免剧烈的适应,从而缓解这一问题。我们表明,使用不对称更新规则促使新类别适应旧的类别(而不是逆向),这在任务界限上更为有效,因为通常会发生遗忘的情况。