Introducing a time dependency on the data generating distribution has proven to be difficult for gradient-based training of neural networks, as the greedy updates result in catastrophic forgetting of previous timesteps. Continual learning aims to overcome the greedy optimization to enable continuous accumulation of knowledge over time. The data stream is typically divided into locally stationary distributions, called tasks, allowing task-based evaluation on held-out data from the training tasks. Contemporary evaluation protocols and metrics in continual learning are task-based and quantify the trade-off between stability and plasticity only at task transitions. However, our empirical evidence suggests that between task transitions significant, temporary forgetting can occur, remaining unidentified in task-based evaluation. Therefore, we propose a framework for continual evaluation that establishes per-iteration evaluation and define a new set of metrics that enables identifying the worst-case performance of the learner over its lifetime. Performing continual evaluation, we empirically identify that replay suffers from a stability gap: upon learning a new task, there is a substantial but transient decrease in performance on past tasks. Further conceptual and empirical analysis suggests not only replay-based, but also regularization-based continual learning methods are prone to the stability gap.

本论文提出一个连续的学习框架，旨在解决神经网络训练过程中遗忘的问题，并定义了一系列新的指标来实现对学习机的实时评估，研究表明既有重放机制又有正则化机制的在线学习方法都存在稳定性差距的问题。

终身学习中的持续评估：发现稳定性差距