持续学习与灾难性遗忘
Continual Learning and Catastrophic Forgetting
AI总结：
本书章节探讨持续学习从非平稳数据流增量学习的挑战，重点分析灾难性遗忘现象，并回顾该领域近十年的研究见解。
摘要：
本书章节深入探讨了持续学习的动态过程，即从非平稳数据流中增量学习的过程。尽管持续学习是人脑的自然技能，但对人工神经网络而言却极具挑战性。一个重要原因是，在学习新知识时，这些网络往往会迅速且剧烈地遗忘之前所学的内容，这一现象被称为灾难性遗忘。尤其是在过去十年中，持续学习已成为深度学习领域广泛研究的课题。本书章节回顾了该领域所产生的见解。
英文摘要：
This book chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a non-stationary stream of data. Although continual learning is a natural skill for the human brain, it is very challenging for artificial neural networks. An important reason is that, when learning something new, these networks tend to quickly and drastically forget what they had learned before, a phenomenon known as catastrophic forgetting. Especially in the last decade, continual learning has become an extensively studied topic in deep learning. This book chapter reviews the insights that this field has generated.
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。
