Frontiers of Information Technology & Electronic Engineering
>> 2018,
Volume 19,
Issue 1
doi:
10.1631/FITEE.1700786
Article
Generative adversarial network based novelty detection usingminimized reconstruction error
Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing 100084, China.
Available online: 2018-04-23
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Abstract
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 and squared prediction error statistics.
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