Retraction published on 15 January 2020, see
Sensors 2020, 20(2), 476.
The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN
1
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
2
School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China
3
Hunan Institute of Scientific and Technical Information, Changsha 410001, China
4
Electronics & Information School, Yangtze University, Jingzhou 434023, China
5
Technical Quality Department, Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited, Changsha 410005, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3145; https://doi.org/10.3390/s19143145
Received: 16 June 2019
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Revised: 12 July 2019
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Accepted: 16 July 2019
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Published: 17 July 2019
(This article belongs to the Special Issue Intelligent Sensor Signal in Machine Learning)
Simple Summary
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.
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Keywords:
generative adversarial networks (GAN); auxiliary classifier generative adversarial networks (ACGAN); feature matching; image classification; CP-ACGAN
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MDPI and ACS Style
Chen, Y.; Tao, J.; Wang, J.; Chen, X.; Xie, J.; Xiong, J.; Yang, K. The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN. Sensors 2019, 19, 3145. https://doi.org/10.3390/s19143145
AMA Style
Chen Y, Tao J, Wang J, Chen X, Xie J, Xiong J, Yang K. The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN. Sensors. 2019; 19(14):3145. https://doi.org/10.3390/s19143145
Chicago/Turabian StyleChen, Yuantao, Jiajun Tao, Jin Wang, Xi Chen, Jingbo Xie, Jie Xiong, and Kai Yang. 2019. "The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN" Sensors 19, no. 14: 3145. https://doi.org/10.3390/s19143145
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