The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN
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...
| Main Authors: | , , , , , , |
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| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/14/3145 |
| _version_ | 1811187115421073408 |
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| author | Yuantao Chen Jiajun Tao Jin Wang Xi Chen Jingbo Xie Jie Xiong Kai Yang |
| author_facet | Yuantao Chen Jiajun Tao Jin Wang Xi Chen Jingbo Xie Jie Xiong Kai Yang |
| author_sort | Yuantao Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T13:56:45Z |
| format | Article |
| id | doaj.art-1607cea11bbf45259a3857956f8903c5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:56:45Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1607cea11bbf45259a3857956f8903c52022-12-22T04:20:13ZengMDPI AGSensors1424-82202019-07-011914314510.3390/s19143145s19143145The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGANYuantao Chen0Jiajun Tao1Jin Wang2Xi Chen3Jingbo Xie4Jie Xiong5Kai Yang6School 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, ChinaSchool 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, ChinaSchool 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, ChinaSchool 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, ChinaHunan Institute of Scientific and Technical Information, Changsha 410001, ChinaElectronics & Information School, Yangtze University, Jingzhou 434023, ChinaTechnical Quality Department, Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited, Changsha 410005, ChinaTo 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.https://www.mdpi.com/1424-8220/19/14/3145generative adversarial networks (GAN)auxiliary classifier generative adversarial networks (ACGAN)feature matchingimage classificationCP-ACGAN |
| spellingShingle | Yuantao Chen Jiajun Tao Jin Wang Xi Chen Jingbo Xie Jie Xiong Kai Yang The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN Sensors generative adversarial networks (GAN) auxiliary classifier generative adversarial networks (ACGAN) feature matching image classification CP-ACGAN |
| title | The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN |
| title_full | The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN |
| title_fullStr | The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN |
| title_full_unstemmed | The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN |
| title_short | The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN |
| title_sort | novel sensor network structure for classification processing based on the machine learning method of the acgan |
| topic | generative adversarial networks (GAN) auxiliary classifier generative adversarial networks (ACGAN) feature matching image classification CP-ACGAN |
| url | https://www.mdpi.com/1424-8220/19/14/3145 |
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