摘要
生成对抗网络(Generative adversarial networks,GAN)是目前热门的生成式模型.深度卷积生成对抗网络(Deep convolutional GAN,DCGAN)在传统生成对抗网络的基础上,引入卷积神经网络(Convolutional neural networks,CNN)进行无监督训练;条件生成对抗网络(Conditional GAN,CGAN)在GAN的基础上加上条件扩展为条件模型.结合深度卷积生成对抗网络和条件生成对抗网络的优点,建立条件深度卷积生成对抗网络模型(Conditional-DCGAN,C-DCGAN),利用卷积神经网络强大的特征提取能力,在此基础上加以条件辅助生成样本,将此结构再进行优化改进并用于图像识别中,实验结果表明,该方法能有效提高图像的识别准确率.
Generative adversarial network（GAN） is a prevalent generative model. Deep convolutional generative adversarial network（DCGAN）, based on traditional generative adversarial networks, introduces convolutional neural networks（CNN） into the training for unsupervised learning to improve the effect of generative networks. Conditional generative adversarial network（CGAN） is a conditional model which adds condition extension into GAN. The generative model of conditional-DCGAN（C-DCGAN） is a combination of DCGAN and CGAN, which integrates the feature extraction of convolutional networks and condition auxiliary generative sample for image recognition. The result of simulation experiments shows that this model can improve the accuracy of image recognition.
作者
唐贤伦
杜一铭
刘雨微
李佳歆
马艺玮
TANG Xian-Lun;DU Yi-Ming;LIU Yu-Wei;LI Jia-Xin;MA Yi-Wei(College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065;College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065)
出处
《自动化学报》
EI
CSCD
北大核心
2018年第5期855-864,共10页
Acta Automatica Sinica
基金
国家自然科学基金(61673079
61703068)
重庆市基础科学与前沿技术研究项目(cstc2016jcyj A1919)资助~~
关键词
生成对抗网络
卷积神经网络
条件模型
特征提取
图像识别
Generative adversarial network （GAN）
convolutional neural networks （CNN）
conditional models
featureextraction
image recognition
