乔-SNC:通过促进自我和邻居的一致性,打击噪音标签
IEEE transactions on pattern analysis and machine intelligence
|December 22, 2025
概括
本研究介绍了Jo-SNC,这是一种新的方法,可以通过共同选择样本和规范化模型来打击深度学习中的标签噪声,使用自我和邻居一致性,提高对损坏数据的稳定性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 标签噪声在监督深度学习中是一个重大挑战,因为深度网络容易记住受损的样本.
- 现有的处理标签噪声的方法往往忽视了微批量噪声分布的不平衡,并与分布外的噪声数据作斗争.
研究的目的:
- 开发一种强大的方法,Jo-SNC (基于自我和邻居一致性的联合样本选择和模型规范化),以有效地解决深度学习中的标签噪声.
- 改善分销和分销之外的噪音数据的识别和处理.
主要方法:
- 使用Jensen-Shannon分歧来评估样本清洁或分布外的可能性,并纳入最近的邻居以提高可靠性.
- 实施自适应的,数据驱动的门方案,用于每个类别的选择,并应用部分标签学习用于分布式噪音和负面学习用于分布式噪音.
- 引入三重一致性规范化以加强自我预测,邻居预测和特征一致性.
主要成果:
- 与最先进的方法相比,Jo-SNC在各种基准数据集中表现出卓越的性能.
- 广泛的废弃研究证实了在处理标签噪声方面提出的方法的有效性和稳定性.
结论:
- 通过有效识别和处理不同类型的噪音数据,Jo-SNC在噪音强大的深度学习方面取得了重大进展.
- 该方法能够处理标签噪声,包括分发之外的样本,使其成为该领域的宝贵贡献.
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