在分配转移下选择性分类
Hengyue Liang1, Le Peng2, Ju Sun2
1Department of Electrical and Computer Engineering, University of Minnesota.
概括
选择性分类 (SC) 对于在高风险场景中部署不完美的AI模型至关重要. 本研究引入了通用选择性分类,以处理现实世界的数据分布转移,提高分类器的可靠性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 选择性分类 (SC) 使人工智能分类器能够避免不确定的预测,这对于高风险的应用至关重要.
- 现有的SC方法往往假定理想的数据分布,未能解决现实世界的部署挑战,如分布转移.
- 不完善的分类器,由于噪音或强度问题,需要先进的SC技术可靠部署.
研究的目的:
- 提出第一个选择性分类框架,称为通用选择性分类 (GSC),该框架明确地解决了数据分布的转变.
- 为 GSC 开发针对深度学习 (DL) 分类器量身定制的新型,非培训为基础的信心评分功能.
- 在实际的,非分销场景中提高SC的可靠性和有效性.
主要方法:
- 开发了一种通用选择性分类 (GSC) 框架,用于处理分布式,标签转移和共变量转移样本.
- 提出了两种新的基于边际的信任评分功能,专门用于使用深度学习模型的GC.
- 专注于非基于培训的评分功能,以避免再培训的复杂性.
主要成果:
- 建议的得分函数表现出优越的有效性和可靠性,与一般化SC的现有方法相比.
- 在各种分类任务和深度学习架构上的实证验证证了框架的性能.
- 该研究提供了一个强大的解决方案,用于在分配班次下部署分类器.
结论:
- 通用选择性分类是部署AI在现实世界,非理想条件的关键进步.
- 新的基于边际的分数函数为深度学习中的GSC提供了可靠的方法.
- 这项工作弥合了理论SC研究和实际部署挑战之间的差距.
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