预测一致性规范化与噪音标签的学习基于对比的集群.
Xinkai Sun1,2, Sanguo Zhang1,2, Shuangge Ma3
1School of Mathematics Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Entropy (Basel, Switzerland)
|April 26, 2024
概括
这项研究引入了一种新的方法,通过提高预测一致性来提高分类准确度,尽管标签噪声. 它使用调整的双对比集群 (TCC) 和基于原型的规范化来识别类似的样本并减轻噪声效应.
科学领域:
- 机器学习 机器学习
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 标签噪声通过破坏预测的一致性和降低准确性,显著降低了神经网络在分类任务中的性能.
- 识别类似的样本对于有效的降噪至关重要,但仍然是现有方法的主要挑战.
研究的目的:
- 开发一种新的预测一致性规范化技术,以减轻标签噪声对神经网络分类的影响.
- 通过将其正式化为一个集群问题来解决识别类似样本的挑战.
主要方法:
- 采用双对比分类 (TCC) 进行类似样本识别,通过调整分类先验与标签信息来增强.
- 基于调整的TCC结果构建了集群原型,并制定了基于原型的规范化术语,以提高预测一致性.
- 在各种噪声率的基准数据集上评估了该方法的有效性.
主要成果:
- 在不同的标签噪声场景下显著提高了分类准确性.
- 证实,拟议的规范化术语有效地减轻了标签噪声的不利影响.
- 证明调整后的TCC可以提高类似样本识别的质量.
结论:
- 新的预测一致性规范化有效地打击了分类任务中的标签噪声.
- 整合调整的TCC和基于原型的规范化提供了一个强大的解决方案,用于在杂的数据环境中改善模型性能.
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