基于知识蒸的补充标签学习
Peng Ying1, Zhongnian Li1, Renke Sun1
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
本研究介绍了基于知识蒸 (KDCL) 的补充标签增强,这是改善弱监督学习的新框架. KDCL使用教师-学生模型增强了互补标签,提高了分类准确性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 补充标签学习 (CLL) 是一种弱监督的方法,使用非目标类信息.
- 现有的CLL方法往往在补充标签中的监管信号使用不足.
- 增强这些信号对于改善CLL性能至关重要.
研究的目的:
- 提出一个新的框架,基于知识蒸 (KDCL) 的补充标签增强,以丰富CLL的监督.
- 解决当前CLL方法在利用补充标签信息方面的局限性.
- 用补充标签训练的模型提高准确性.
主要方法:
- 介绍了KDCL,一个使用教师-学生深度神经网络架构的框架.
- 教师模式软化了补充标签,以加强监督.
- 学生模型从这些软化标签中学习,两者都在补充标签的数据上接受训练.
主要成果:
- 与基线CLL方法相比,KDCL优化的模型在四个数据集 (MNIST,F-MNIST,K-MNIST,CIFAR-10) 中显示出更高的准确性.
- 实验使用了各种数据集和教师-学生模型对 (Lenet-5+MLP,DenseNet-121+ResNet-18).
- 提出的方法有效地提高了分类性能.
结论:
- 通过有效加强监督,KDCL在补充标签学习方面取得了重大进展.
- 在KDCL内部的知识蒸方法成功地丰富了补充标签信息.
- 这一框架为改善低监督学习任务提供了一个有希望的方向.
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