Chen Yuantao, Tao Jiajun, Wang Jin, Chen Xi, Xie Jingbo, Xiong Jie, Yang Kai
School of Computer and Communication Engineering & Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China.
School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China.
Sensors (Basel). 2019 Jul 17;19(14):3145. doi: 10.3390/s19143145.
To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more effective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions.
为了解决基于生成对抗网络（GAN）的图像分类算法中训练不稳定和准确率低的问题，本文提出了一种使用辅助分类器生成对抗网络（ACGAN）进行分类处理的新型传感器网络结构。首先，在判别网络的输出层取消了网络中传感器样本的真/假判别，仅输出样本标签的后验概率估计。其次，将真实传感器样本视为有监督数据，将生成的传感器样本视为有标签的虚假数据，利用传感器样本的真/假属性和标签的交叉熵损失函数，重构了生成器和判别器的损失函数。第三，在判别器中引入了池化和缓存方法，以实现对分类特征更有效的提取。最后，在判别网络中添加了特征匹配，以确保生成的传感器样本的多样性。实验结果表明，所提出的算法（CP-ACGAN）在MNIST数据集、CIFAR10数据集和CIFAR100数据集上比其他解决方案具有更高的分类准确率。此外，与具有与CP-ACGAN相同深度网络结构的ACGAN和CNN分类算法相比，该方法在分类效果和稳定性方面仍优于其他主要现有的传感器解决方案。
