Continual Learning (CL) aims at incrementally learning new tasks without
forgetting the knowledge acquired from old ones. Experience Replay (ER) is a
simple and effective rehearsal-based strategy, which optimizes the model with
current training data and a subset of old samples stored in a memory buffer. To
further reduce forgetting, recent approaches extend ER with various techniques,
such as model regularization and memory sampling. However, the prediction
consistency between the new model and the old one on current training data has
been seldom explored, resulting in less knowledge preserved when few previous
samples are available. To address this issue, we propose a CL method with
Strong Experience Replay (SER), which additionally utilizes future experiences
mimicked on the current training data, besides distilling past experience from
the memory buffer. In our method, the updated model will produce approximate
outputs as its original ones, which can effectively preserve the acquired
knowledge. Experimental results on multiple image classification datasets show
that our SER method surpasses the state-of-the-art methods by a noticeable
margin.

本研究提出了一种基于强化经验回放的连续学习方法，通过使用当前训练数据模仿未来经验，以及蒸馏内存缓冲区的过去经验，来提高模型的预测一致性，从而有效保留已获得的知识。实验结果表明，我们的方法在多个图像分类数据集上优于现有方法。