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。

强化经验重放的连续学习

Continual Learning with Strong Experience Replay

Continual learning, the setting where a learning agent is faced with a never
ending stream of data, continues to be a great challenge for modern machine
learning systems. In particular the online or "single-pass through the data"
setting has gained attention recently as a natural setting that is difficult to
tackle. Methods based on replay, either generative or from a stored memory,
have been shown to be effective approaches for continual learning, matching or
exceeding the state of the art in a number of standard benchmarks. These
approaches typically rely on randomly selecting samples from the replay memory
or from a generative model, which is suboptimal. In this work, we consider a
controlled sampling of memories for replay. We retrieve the samples which are
most interfered, i.e. whose prediction will be most negatively impacted by the
foreseen parameters update. We show a formulation for this sampling criterion
in both the generative replay and the experience replay setting, producing
consistent gains in performance and greatly reduced forgetting. We release an
implementation of our method at
this https URL

该论文探讨了在机器学习系统中，面对一个不断涌现的数据流的情况下，通过回放的方法来控制采样记忆，从而提供全新的方案来解决单通道数据训练面临的挑战，并通过多个标准基准测试，达到了与或超过现有最先进技术的表现。