关于深度主动学习的调查:最近的进展和新的前沿
概括
深度主动学习 (DAL) 使用人类反来有效地训练模型. 这份调查提供了对DAL方法,应用和该领域未来研究方向的全面指南.
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:
- 积极学习通过反复查询标签的预言器来提高更少的训练样本的模型性能.
- 深度主动学习 (DAL) 是一个快速增长的子领域,可用的综合调查有限.
- 现有的文献缺乏对DAL方法和应用的结构化概述.
研究的目的:
- 为深度主动学习 (DAL) 提供先进和全面的调查.
- 系统地分类和分析现有的DAL方法.
- 引导研究人员应对挑战,并确定DAL.的未来研究途径.
主要方法:
- 进行了对相关学术论文的彻底审查和过.
- 正式定义了DAL任务,概述了关键的基线和数据集.
- 根据注释类型,查询策略,模型架构,学习范式和培训流程开发了DAL方法的分类.
主要成果:
- 提出了DAL技术的系统分类,分析了它们各自的优缺点.
- 总结了DAL在自然语言处理 (NLP),计算机视觉 (CV) 和数据挖掘 (DM) 中的主要应用.
- 确定了当前的挑战,并概述了DAL领域的未来研究前景.
结论:
- 这项调查对进入深度主动学习领域的研究人员来说是一个至关重要的资源.
- 它提供了对DAL方法的结构化理解,促进了进一步的进步.
- 这项工作旨在加速在高效机器学习模型培训勃发展的领域的进展.
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