基于条件深度卷积生成对抗网络的图像识别方法

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 adver-sarial 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 con-volutional 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.