提出流式持续学习新范式，兼顾快速适应与记忆保留。
A Practical Guide to Streaming Continual Learning
- 融合持续学习与流式机器学习，统一应对数据变化挑战。
- 实验表明单一方法难以同时实现快速适应与知识保留。
- 适合需要长期学习与动态更新的现实场景应用。
持续学习（CL）与流式机器学习（SML）研究智能体从非平稳数据流中持续学习的能力。尽管两者有相似之处，但解决的是不同且互补的挑战：SML关注概念漂移后的快速适应，而CL强调在学习新任务时保留旧知识。本文简要介绍CL与SML后，探讨了新兴的流式持续学习（SCL）范式，该范式为现实世界问题提供统一解决方案，可能同时需要SML与CL能力。我们主张SCL可连接CL与SML研究社区，推动其共同目标；并促进设计兼具快速适应（如SML）与无遗忘学习（如CL）的混合方法。论文以一个激励性案例和一组实验结束，凸显了SCL的必要性：单独使用CL或SML均难以同时实现快速适应与知识保留。
原文摘要 · Abstract (English)
Continual Learning (CL) and Streaming Machine Learning (SML) study the ability of agents to learn from a stream of non-stationary data. Despite sharing some similarities, they address different and complementary challenges. While SML focuses on rapid adaptation after changes (concept drifts), CL aims to retain past knowledge when learning new tasks. After a brief introduction to CL and SML, we discuss Streaming Continual Learning (SCL), an emerging paradigm providing a unifying solution to real-world problems, which may require both SML and CL abilities. We claim that SCL can i) connect the CL and SML communities, motivating their work towards the same goal, and ii) foster the design of hybrid approaches that can quickly adapt to new information (as in SML) without forgetting previous knowledge (as in CL). We conclude the paper with a motivating example and a set of experiments, highlighting the need for SCL by showing how CL and SML alone struggle in achieving rapid adaptation and knowledge retention.
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