Co-training generative adversarial networks for semi-supervised classification method

Zhe XU; Jie GENG; Wen JIANG; Zhuo ZHANG; Qing-jie ZENG

doi:10.37188/OPE.20212905.1127

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Co-training generative adversarial networks for semi-supervised classification method

Information Sciences | 更新时间：2021-07-08

- Co-training generative adversarial networks for semi-supervised classification method
- Optics and Precision Engineering Vol. 29, Issue 5, Pages: 1127-1135(2021)
- 作者机构：
  
  西北工业大学电子信息学院，西安 710072
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20212905.1127
  CLC： TP391
- Received：04 November 2020，
  
  Revised：04 January 2021，
  
  Published：15 May 2021
- 稿件说明：
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徐哲,耿杰,蒋雯等.联合训练生成对抗网络的半监督分类方法[J].光学精密工程,2021,29(05):1127-1135.

XU Zhe,GENG Jie,JIANG Wen,et al.Co-training generative adversarial networks for semi-supervised classification method[J].Optics and Precision Engineering,2021,29(05):1127-1135.
徐哲,耿杰,蒋雯等.联合训练生成对抗网络的半监督分类方法[J].光学精密工程,2021,29(05):1127-1135. DOI： 10.37188/OPE.20212905.1127.

XU Zhe,GENG Jie,JIANG Wen,et al.Co-training generative adversarial networks for semi-supervised classification method[J].Optics and Precision Engineering,2021,29(05):1127-1135. DOI： 10.37188/OPE.20212905.1127.

摘要

深度神经网络需要大量数据进行监督训练学习，而实际应用中往往难以获取大量标签数据。半监督学习可以减小深度网络对标签数据的依赖，基于半监督学习的生成对抗网络可以提升分类效果，但仍存在训练不稳定的问题。为进一步提高网络的分类精度并解决网络训练不稳定的问题，本文提出一种基于联合训练生成对抗网络的半监督分类方法，通过两个判别器的联合训练来消除单个判别器的分布误差，同时选取无标签数据中置信度高的样本来扩充标签数据集，提高半监督分类精度并提升网络模型的泛化能力。在CIFAR-10和SVHN数据集上的实验结果表明，本文方法在不同数量的标签数据下都获得更好的分类精度。当标签数量为2 000时，在CIFAR-10数据集上分类精度可达80.36%；当标签数量为10时，相比于现有的半监督方法，分类精度提升了约5%。在一定程度上解决了GAN网络在小样本条件下的过拟合问题。

Abstract

Deep neural networks require a large amount of data for supervised learning； however， it is difficult to obtain enough labeled data in practical applications. Semi-supervised learning can train deep neural networks with limited samples. Semi-supervised generative adversarial networks can yield superior classification performance； however， they are unstable during training in classical networks. To further improve the classification accuracy and solve the problem of training instability for networks， we propose a semi-supervised classification model called co-training generative adversarial networks （CT-GAN） for image classification. In the proposed model， co-training of two discriminators is applied to eliminate the distribution error of a single discriminator and unlabeled samples with higher confidence are selected to expand the training set， which can be utilized for semi-supervised classification and enhance the generalization of deep networks. Experimental results on the CIFAR-10 dataset and the SVHN dataset showed that the proposed method achieved better classification accuracies with different numbers of labeled data. The classification accuracy was 80.36% with 2000 labeled data on the CIFAR-10 dataset， whereas it improved by about 5% compared with the existing semi-supervised method with 10 labeled data. To a certain extent， the problem of GAN overfitting under a few sample conditions is solved.

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Keywords

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