半参数记忆巩固：迈向类脑深度持续学习
Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning
AI总结：
受人类记忆系统启发，提出整合半参数记忆与醒睡巩固机制的仿生持续学习框架，在ImageNet类增量学习等挑战场景中，使深度神经网络在保持旧知识的同时高效学习新任务。
摘要：
人类和大多数动物天生具备一种独特的能力，能够持续获取新经验并随时间积累关于世界的知识。这种能力被称为持续学习，对于深度神经网络（DNNs）在开放环境中适应动态演变的世界也至关重要。然而，DNNs在按顺序训练任务时，会严重遭受对先前所学知识的灾难性遗忘。在这项工作中，受人类交互式记忆与学习系统的启发，我们提出了一种新颖的仿生持续学习框架，该框架整合了半参数记忆与醒睡巩固机制。我们的方法首次使深度神经网络能够在真实世界具有挑战性的持续学习场景（例如，在ImageNet上的类增量学习）中，在保持先前知识的同时，在新任务上保持高性能。这项研究表明，模拟生物智能为赋予深度神经网络持续学习能力提供了一条有前景的路径。
英文摘要：
Humans and most animals inherently possess a distinctive capacity to continually acquire novel experiences and accumulate worldly knowledge over time. This ability, termed continual learning, is also critical for deep neural networks (DNNs) to adapt to the dynamically evolving world in open environments. However, DNNs notoriously suffer from catastrophic forgetting of previously learned knowledge when trained on sequential tasks. In this work, inspired by the interactive human memory and learning system, we propose a novel biomimetic continual learning framework that integrates semi-parametric memory and the wake-sleep consolidation mechanism. For the first time, our method enables deep neural networks to retain high performance on novel tasks while maintaining prior knowledge in real-world challenging continual learning scenarios, e.g., class-incremental learning on ImageNet. This study demonstrates that emulating biological intelligence provides a promising path to enable deep neural networks with continual learning capabilities.
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。
