重新审视信心估计:朝着可靠的故障预测.
IEEE transactions on pattern analysis and machine intelligence
|December 13, 2023
概括
大多数深度学习的信心方法阻碍了错误检测. 这项研究表明,它们会使错误分类数据的信任分离变得更糟. 我们建议找到平面最小值来扩大这个差距,改善可靠AI的故障预测.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度神经网络 深度神经网络
背景情况:
- 对于风险敏感的人工智能应用程序来说,可靠的信心估计是至关重要的.
- 深度神经网络经常表现出对错误预测和分布外 (OOD) 样本的过度信心.
- 现有的信心校准和OOD检测方法有局限性.
研究的目的:
- 识别和解决常用信任估计方法对错误分类错误检测的不利影响.
- 为了改善正确分类和错误分类样本之间的可靠性分离.
- 在现实场景中提高深度学习模型的可靠性.
主要方法:
- 研究了信心估计方法损害故障预测的现象.
- 分析了流行的校准和OOD检测技术如何影响信心分离.
- 提出了一种寻找平面最小值以扩大信任差距的新方法.
主要成果:
- 证明大多数信心估计方法对错误分类检测有负面影响.
- 展示了流行的方法减少了正确和不正确预测之间的信心分离.
- 在各种分类设置中使用平面最小值方法实现了最先进的故障预测性能.
结论:
- 寻找平面最小值的拟议方法显著改善了故障预测.
- 这项工作为深度学习中可靠的信心估计提供了强有力的基准.
- 该研究弥合了模型校准,OOD检测和故障预测之间的理解.
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