CoDC:通过分歧和一致性,通过噪音标签准确学习
Yongfeng Dong1,2,3, Jiawei Li1,2,3, Zhen Wang1,2,3
1School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China.
Biomimetics (Basel, Switzerland)
|February 23, 2024
概括
本研究介绍了CoDC,这是一种用于深度神经网络 (DNN) 的新方法,可以准确地从噪音标签中学习. CoDC通过结合特征级别的分歧和预测级别的一致性策略来增强概括性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 深度神经网络 (DNN) 在许多任务中表现出色,但与标签噪音作斗争.
- 对于杂的标签,现有的联合教学方法对于一般化是样本效率低下的.
- 标签噪声可能会对DNN的记忆和性能产生负面影响.
研究的目的:
- 提出CoDC,一种新的联合教学方法,以准确学习与标签噪音.
- 在杂的场景中使用分歧和一致性策略来提高概括性能.
- 利用大损失样本的知识来增强学习.
主要方法:
- CoDC使用平衡损失函数来保持功能级别的异议和预测级别的一致性.
- 使用加权的交叉损失,根据历史训练数据提供信息.
- 伪标签被分配给大损失样本,以利用他们的知识.
主要成果:
- 在服装1M数据集上,CoDC实现了72.81%的准确性.
- 在WebVision1.0数据集上,CoDC获得了76.96%的Top-1准确度.
- 实验表明,在合成和现实世界的噪音类型中,性能优越.
结论:
- CoDC有效地解决了在DNN中使用噪音标签学习的挑战.
- 拟议的方法显示了稳定性和改进的概括能力.
- 在杂的环境中,CoDC提供了一种可靠的深度学习方法.
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