一个时间一致的课程,从依赖实例的噪音标签学习
IEEE transactions on pattern analysis and machine intelligence
|February 1, 2024
概括
这项研究引入了一种新的方法来训练具有依赖实例的噪声标签的机器学习模型,通过联合训练一个噪声逆转模型和一个强大的分类器. 一个时间一致性的课程可以提高在具有挑战性的噪音标签场景中的表现.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 机器学习算法经常与杂的标签扎,特别是当噪音依赖实例时.
- 现实世界的噪音标签源于复杂的,实例特定的机制,对模型稳定性构成重大挑战.
研究的目的:
- 为了应对机器学习中依赖实例的噪音标签的挑战.
- 开发一种新的端到端培训方法,用于从噪音数据中进行可靠的分类.
主要方法:
- 共同培训一个可以逆转噪声产生机制的模型和一个强大的分类器.
- 使用清洁和噪音标签之间的依赖实例映射.
- 开发一个时间一致性课程,根据模型输出动态选择信息培训数据.
主要成果:
- 拟议的方法证明了在依赖实例的噪音标签上强大的分类性能.
- 时间一致性课程通过提供高质量的数据显著增强了培训.
- 在挑战杂的标签设置方面超越了最先进的方法.
结论:
- 一个噪音逆转模型和一个强大的分类器的联合训练对于依赖实例的噪音标签是有效的.
- 数据的时间一致性对于成功的培训至关重要,基于它的课程可以提高绩效.
- 该方法为具有固有的数据噪声的真实世界机器学习应用提供了有前途的解决方案.
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