一个改进的样本选择框架,用于学习与杂的标签
Qian Zhang1, Yi Zhu1, Ming Yang2
1School of Information Technology, Jiangsu Open University, Nanjing, Jiangsu, China.
PloS one
|December 5, 2024
概括
这项研究引入了一种新的过量采样策略 (SOS),以改进在噪音标签上训练的深度学习模型. 通过弥合样本选择方法的差距,SOS有效地利用未标记的数据,提高分类和概括性能.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 深度神经网络 (DNN) 擅长学习,但在训练有噪音标签时容易过度适应.
- 现有的样本选择方法可以过潜在的清洁标签,但在标签和未标签数据之间产生很大的差距,特别是在高噪音率下.
- 这种未标记数据的不足利用限制了噪音标签学习中的性能改进.
研究的目的:
- 引入一个增强的样本选择框架,并采用超样本策略 (SOS),以解决当前方法的局限性.
- 利用无标签实例的信息来提高在有噪音标签的情况下的模型性能.
- 通过整合SOS来增强最先进的样本选择技术.
主要方法:
- 开发一个新的超标采样策略 (SOS),将其整合到样本选择框架中.
- 将SOS与现有的最先进的样本选择方法结合起来.
- 在合成和现实世界的噪音数据集 (CIFAR,WebVision,Clothing1M) 上进行了广泛的实验验证.
主要成果:
- 拟议的SOS框架有效地利用了来自无标签实例的信息.
- 与基线方法相比,在分类和概括方面观察到显著的性能改善.
- 在不同噪音水平的不同数据集中,SOS表现出强度.
结论:
- 在深度学习中,SOS框架为减轻噪音标签的负面影响提供了一个有希望的解决方案.
- 通过过量采样利用未标记的数据对于提高模型稳定性和性能至关重要.
- 该研究提供了一种实用的方法和开源代码,用于改进对噪音数据集的深度学习.
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