Image Recognition With Conditional Deep Convolutional Generative Adversarial Networks
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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.Abstract: 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.1) 本文责任编委 李力
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表 1 MNIST上各方法准确率对比
Table 1 The recognition accuracy comparison on MNIST
识别方法 预训练 准确率(%) linear classifier (1-layer NN) 去斜 91.60 K-nearest-neighbors, Euclidean (L2) - 95.00 40 PCA+quadratic classifier - 96.70 SVM, Gaussian Kernel - 98.60 Trainable feature extractor+SVMs [no distortions] - 99.17 Convolutional net LeNet-5, [no distortions] - 99.05 Convolutional net LeNet-5, [huge, distortions] huge distortions 99.15 Convolutional net LeNet-5, [distortions] distortions 99.20 CNN 归一化 98.40 C-DCGAN+Softmax - 99.45 
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表 2 CIFAR-10上各方法准确率对比
Table 2 The recognition accuracy comparison on CIFAR-10
识别方法 准确率(%) 1 Layer K-means 80.6 3 Layer K-means Learned RF 82.0 View Invariant K-means 81.9 Cuda-convnet (CNN) 82.0 DCGAN+L2-SVM 82.8 C-DCGAN+Softmax 84
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