Full Text:   <412>

Summary:  <193>

CLC number: TP391

On-line Access: 2018-03-10

Received: 2017-11-24

Revision Accepted: 2018-01-26

Crosschecked: 2018-01-26

Cited: 0

Clicked: 2509

Citations:  Bibtex RefMan EndNote GB/T7714


Huan-gang Wang


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.1 P.116-125


Generative adversarial network based novelty detection using minimized reconstruction error

Author(s):  Huan-gang Wang, Xin Li, Tao Zhang

Affiliation(s):  Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing 100084, China

Corresponding email(s):   hgwang@tsinghua.edu.cn, xin-li16@mails.tsinghua.edu.cn, taozhang@tsinghua.edu.cn

Key Words:  Generative adversarial network (GAN), Novelty detection, Tennessee Eastman (TE) process

Huan-gang Wang, Xin Li, Tao Zhang. Generative adversarial network based novelty detection using minimized reconstruction error[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(1): 116-125.

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T1 - Generative adversarial network based novelty detection using minimized reconstruction error
A1 - Huan-gang Wang
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DOI - 10.1631/FITEE.1700786

generative adversarial network (GAN) is the most exciting machine learning breakthrough in recent years, and it trains the learning model by finding the Nash equilibrium of a two-player zero-sum game. GAN is composed of a generator and a discriminator, both trained with the adversarial learning mechanism. In this paper, we introduce and investigate the use of GAN for novelty detection. In training, GAN learns from ordinary data. Then, using previously unknown data, the generator and the discriminator with the designed decision boundaries can both be used to separate novel patterns from ordinary patterns. The proposed GAN-based novelty detection method demonstrates a competitive performance on the MNIST digit database and the Tennessee Eastman (TE) benchmark process compared with the PCA-based novelty detection methods using Hotelling’s T2 and squared prediction error statistics.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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