提出流式持续学习框架，让模型实时适应变化数据且不遗忘旧知识。
Streaming Continual Learning for Unified Adaptive Intelligence in Dynamic Environments
- 统一持续学习与流式机器学习，实现快速适应新数据。
- 在非平稳数据流中保持旧知识，避免灾难性遗忘。
- 适合需要长期在线学习的智能系统开发者。
在持续产生数据且不断变化的动态环境中，构建有效的预测模型极具挑战。持续学习（CL）和流式机器学习（SML）是应对这一难题的两个研究方向。本文提出一种统一设置，融合两者优势：在不遗忘已有知识的前提下，快速适应非平稳数据流。该设置称为流式持续学习（SCL）。SCL并非替代CL或SML，而是扩展两者的理论与方法体系。文章首先简述CL与SML，并统一其表述语言；随后阐述SCL的核心特征；最后强调连接两大研究社区对推动智能系统发展的重要性。
原文摘要 · Abstract (English)
Developing effective predictive models becomes challenging in dynamic environments that continuously produce data and constantly change. Continual Learning (CL) and Streaming Machine Learning (SML) are two research areas that tackle this arduous task. We put forward a unified setting that harnesses the benefits of both CL and SML: their ability to quickly adapt to non-stationary data streams without forgetting previous knowledge. We refer to this setting as Streaming Continual Learning (SCL). SCL does not replace either CL or SML. Instead, it extends the techniques and approaches considered by both fields. We start by briefly describing CL and SML and unifying the languages of the two frameworks. We then present the key features of SCL. We finally highlight the importance of bridging the two communities to advance the field of intelligent systems.
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