在持续学习中进行探索和利用
Kiseong Hong1, Hyundong Jin2, Sungho Suh3
1Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea.
概括
本研究介绍了一种适应性探索-利用方法,用于持续学习 (CL). 它优化了对新任务的参数分配,显著优于现有的CL方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 持续学习 (CL) 旨在通过参数隔离来防止灾难性遗忘.
- 现有的方法经常使用固定的参数比率,忽视特定任务的需求,导致性能低于最佳.
研究的目的:
- 开发一种新的探索-利用方法,用于持续学习中的适应性参数分配.
- 让持续学习者能够动态地平衡获取新信息 (探索) 和保留旧知识 (利用).
主要方法:
- 引入了一个适应性的探索-利用战略,用于持续学习中的资源分配.
- 开发了一个分配学习器,以管理所有层次的勘探和开采之间的相互作用.
- 在各种CL场景中对图像分类基准的方法进行了评估,包括域移动任务增量学习.
主要成果:
- 拟议的勘探开发方法适应地为每个新任务分配参数.
- 与现有的持续学习方法相比,表现出优异的表现.
- 在各种场景中实现了5.3%的平均绩效改进.
结论:
- 使用探索-利用的自适应参数分配对持续学习是有效的.
- 拟议的方法解决了固定参数比率方法的局限性.
- 这项工作通过优化各种任务的资源管理来推进持续学习.
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