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相关实验视频

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在使用噪音标签学习时,基于DNN的不变特征的标签校正.

Lihui Deng1, Bo Yang1, Zhongfeng Kang2

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.

Neural networks : the official journal of the International Neural Network Society
|February 3, 2024
PubMed
概括

这项研究引入了基于不变特征的标签校正 (IFLC),以改善使用噪音标签训练的深度神经网络 (DNN). IFLC通过利用不变特征来减少虚假特征,以实现更准确的标签校正.

关键词:
深度神经网络是一个神经网络.不变的特征是不变的特征.标签纠正 标签纠正学习与噪音标签的学习

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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) 与包含错误标记数据的数据集进行斗争.
  • 现有的噪音标签学习 (LNL) 方法通常依赖于虚假的语义特征,限制了它们的有效性.
  • 跨域的不稳定的特征标签相关性可能会损害LNL方法的性能.

研究的目的:

  • 提出基于不变特征的标签校正 (IFLC),这是一种解决LNL虚假特征的新方法.
  • 通过使用具有稳定相关性的不变特征来提高标签噪声校正的准确性.
  • 通过减少对不稳定的语义特征的依赖来缓解当前LNL方法的局限性.

主要方法:

  • 基于不变特征的标签校正 (IFLC) 框架.
  • 标签扰乱 (LD) 过程,以鼓励跨环境稳定的DNN性能,并减少虚假特征.
  • 代表性脱相关 (RD) 过程,以增强特征表示中的独立性,以便准确的标签校正.
  • 强大的线性回归应用于标签校正的特征表示.

主要成果:

  • 通过鼓励稳定的DNN性能,IFLC有效地减少了虚假特征.
  • 在RD过程中,可以准确地利用已学习的不变特征进行标签校正.
  • 关于CIFAR-10,CIFAR-100,Animal-10N和Clothing1M数据集的实验结果表明,与最先进的NLN方法相比,它们具有竞争力或更高的性能.

结论:

  • 通过解决虚假特征问题,IFLC提供了一种新的学习与噪音标签的方法.
  • 拟议的方法通过利用不变特征和脱相关表示实现了强大的标签校正.
  • IFLC 代表了在带有标签噪声的数据集上改善 DNN 性能方面取得的重大进展.