当元学习遇上在线与持续学习：综述
When Meta-Learning Meets Online and Continual Learning: A Survey
AI总结：
本文系统综述了元学习与在线学习、持续学习相结合的研究，统一术语并形式化描述各类问题设置，以促进该领域发展。
摘要：
在过去十年中，深度神经网络通过在大规模数据集上采用小批量随机梯度下降的训练方案，取得了显著成功。在这一成就的基础上，探索神经网络在其他学习场景中应用的研究激增。其中一个备受关注的著名框架是元学习。元学习常被描述为“学会学习”，是一种数据驱动的方法，用于优化学习算法。其他感兴趣的分支是持续学习和在线学习，两者都涉及用流式数据增量更新模型。虽然这些框架最初是独立开发的，但最近的研究已开始探索它们的组合，提出了新颖的问题设置和学习算法。然而，由于复杂性较高且缺乏统一的术语，即使对于经验丰富的研究人员来说，辨别这些学习框架之间的差异也可能具有挑战性。为了促进清晰的理解，本文提供了一篇全面的综述，使用一致的术语和形式化描述来组织各种问题设置。通过提供这些学习范式的概述，我们的工作旨在促进这一有前景的研究领域的进一步发展。
英文摘要：
Over the past decade, deep neural networks have demonstrated significant success using the training scheme that involves mini-batch stochastic gradient descent on extensive datasets. Expanding upon this accomplishment, there has been a surge in research exploring the application of neural networks in other learning scenarios. One notable framework that has garnered significant attention is meta-learning. Often described as "learning to learn," meta-learning is a data-driven approach to optimize the learning algorithm. Other branches of interest are continual learning and online learning, both of which involve incrementally updating a model with streaming data. While these frameworks were initially developed independently, recent works have started investigating their combinations, proposing novel problem settings and learning algorithms. However, due to the elevated complexity and lack of unified terminology, discerning differences between the learning frameworks can be challenging even for experienced researchers. To facilitate a clear understanding, this paper provides a comprehensive survey that organizes various problem settings using consistent terminology and formal descriptions. By offering an overview of these learning paradigms, our work aims to foster further advancements in this promising area of research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
