Full Text:   <272>

Summary:  <96>

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: 686

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Huan-gang Wang

http://orcid.org/0000-0002-7322-3446

-   Go to

Article info.
Open peer comments

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

10.1631/FITEE.1700786


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(11): 116-125.

@article{title="Generative adversarial network based novelty detection using minimized reconstruction error",
author="Huan-gang Wang, Xin Li, Tao Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="1",
pages="116-125",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700786"
}

%0 Journal Article
%T Generative adversarial network based novelty detection using minimized reconstruction error
%A Huan-gang Wang
%A Xin Li
%A Tao Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 1
%P 116-125
%@ 1869-1951
%D 2018
%I Zhejiang University Press & Springer

TY - JOUR
T1 - Generative adversarial network based novelty detection using minimized reconstruction error
A1 - Huan-gang Wang
A1 - Xin Li
A1 - Tao Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 1
SP - 116
EP - 125
%@ 1869-1951
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -


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 T2 and squared prediction error statistics.

基于最小化重构误差的生成对抗网络异常检测

概要:生成对抗网络是机器学习领域近年来最令人瞩目的进展,它通过在二人零和博弈中达到纳什均衡来训练模型。生成对抗网络由一个生成器和一个判别器构成,二者通过对抗学习机制进行训练。本文引入并调查了生成对抗网络在异常检测中的应用。在训练阶段,生成对抗网络从正常数据中学习;然后,基于过去的未知数据,生成器和判别器可以通过学习到的决策边界,区分异常和正常模式。提出的基于生成对抗网络的异常检测方法在MNIST数字数据集和田纳西-伊斯曼标准数据集上的性能表现极具竞争力。

关键词:生成对抗网络;异常检测;田纳西-伊斯曼过程

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

Reference

[1]Abadi M, Andersen D, 2016. Learning to protect communications with adversarial neural cryptography. https://arxiv.org/abs/1610.06918

[2]Arjovsky M, Chintala S, Bottou L, 2017. Wasserstein generative adversarial networks. Int Conf on Machine Learning, p.214-223.

[3]Berthelot D, Schumm T, Metz L, 2017. BEGAN: boundary equilibrium generative adversarial networks. https://arxiv.org/abs/1703.10717

[4]Clifton L, Clifton D, Watkinson P, et al., 2011. Identification of patient deterioration in vital-sign data using one-class support vector machines. Federated Conf on Computer Science and Information Systems, p.125-131.

[5]Denton E, Chintala S, Fergus R, et al., 2015. Deep generative image models using a Laplacian pyramid of adversarial networks. Advances in Neural Information Processing Systems, p.1486-1494.

[6]Donahue J, Krähenbühl P, Darrell T, 2016. Adversarial feature learning. https://arxiv.org/abs/1605.09782

[7]Downs J, Vogel E, 1993. A plant-wide industrial process control problem. Comput Chem Eng, 17(3):245-255.

[8]Dumoulin V, Belghazi I, Poole B, et al., 2016. Adversarially learned inference. https://arxiv.org/abs/1606.00704

[9]Ge Z, Song Z, 2013. Bagging support vector data description model for batch process monitoring. J Proc Contr, 23(8):1090-1096.

[10]Ge Z, Yang C, Song Z, 2009. Improved kernel PCA-based monitoring approach for nonlinear processes. Chem Eng Sci, 64(9):2245-2255.

[11]Ge Z, Gao F, Song Z, 2011. Batch process monitoring based on support vector data description method. J Proc Contr, 21(6):949-959.

[12]Ge Z, Song Z, Gao F, 2013. Review of recent research on data-based process monitoring. Ind Eng Chem Res, 52(10):3543-3562.

[13]Ge Z, Demyanov S, Chen Z, et al., 2017. Generative OpenMax for multi-class open set classification. https://arxiv.org/abs/1707.07418

[14]Goodfellow I, Pouget-Abadie J, Mirza M, et al., 2014. Generative adversarial nets. Advances in Neural Information Processing Systems, p.2672-2680.

[15]Grover A, Ermon S, 2017. Boosted generative models. https://arxiv.org/abs/1702.08484

[16]Hautamaki V, Karkkainen I, Franti P, 2004. Outlier detection using k-nearest neighbour graph. Proc 17th Int Conf on Pattern Recognition, p.430-433.

[17]He Z, Deng S, Xu X, 2005. An optimization model for outlier detection in categorical data. LNCS, 3644:400-409.

[18]Hoffmann H, 2007. Kernel PCA for novelty detection. Patt Recogn, 40(3):863-874.

[19]Kadurin A, Aliper A, Kazennov A, et al., 2017a. The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget, 8(7):10883.

[20]Kadurin A, Nikolenko S, Khrabrov K, et al., 2017b. druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol Pharmaceut, 14(9):3098-3104.

[21]Keogh E, Lonardi S, Ratanamahatana C, 2004. Towards parameter-free data mining. Proc 10th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.206-215.

[22]Kim T, Cha M, Kim H, et al., 2017. Learning to discover cross-domain relations with generative adversarial networks. https://arxiv.org/abs/1703.05192

[23]Ledig C, Theis L, Huszár F, et al., 2016. Photo-realistic single image super-resolution using a generative adversarial network. https://arxiv.org/abs/1609.04802

[24]Li J, Liang X, Wei Y, et al., 2017. Perceptual generative adversarial networks for small object detection. CVPR, p.1951-1959.

[25]Li Y, Maguire L, 2011. Selecting critical patterns based on local geometrical and statistical information. IEEE Trans Patt Anal Mach Intell, 33(6):1189-1201.

[26]Li Y, Liu S, Yang J, et al., 2017. Generative face completion. CVPR, p.5892-5900.

[27]Luc P, Couprie C, Chintala S, et al., 2016. Semantic segmentation using adversarial networks. https://arxiv.org/abs/1611.08408

[28]Mahadevan S, Shah S, 2009. Fault detection and diagnosis in process data using one-class support vector machines. J Proc Contr, 19(10):1627-1639.

[29]Mao X, Li Q, Xie H, et al., 2016. Least squares generative adversarial networks. https://arxiv.org/abs/1611.04076

[30]Mogren O, 2016. C-RNN-GAN: continuous recurrent neural networks with adversarial training. https://arxiv.org/abs/1611.09904

[31]Patcha A, Park J, 2007. An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput Netw, 51(12):3448-3470.

[32]Pimentel M, Clifton D, Clifton L, et al., 2014. A review of novelty detection. Signal Process, 99:215-249.

[33]Radford A, Metz L, Chintala S, 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. https://arxiv.org/abs/1511.06434

[34]Reed S, Akata Z, Yan X, et al., 2016. Generative adversarial text to image synthesis. Proc 33rd Int Conf on Machine Learning, p.1060-1069.

[35]Schlegl T, Seeböck P, Waldstein S, et al., 2017. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. Int Conf on Information Processing in Medical Imaging, p.146-157.

[36]Springenberg J, 2015. Unsupervised and semi-supervised learning with categorical generative adversarial networks. https://arxiv.org/abs/1511.06390

[37]Vondrick C, Pirsiavash H, Torralba A, 2016. Generating videos with scene dynamics. Advances in Neural Information Processing Systems, p.613-621.

[38]Wu J, Zhang C, Xue T, et al., 2016. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. Advances in Neural Information Processing Systems, p.82-90.

[39]Xiao Y, Wang H, Xu W, et al., 2016. Robust one-class SVM for fault detection. Chemometr Intell Lab Syst, 151: 15-25.

[40]Yang Z, Chen W, Wang F, et al., 2017. Improving neural machine translation with conditional sequence generative adversarial nets. https://arxiv.org/abs/1703.04887

[41]Yeh R, Chen C, Lim T, et al., 2016. Semantic image inpainting with perceptual and contextual losses. https://arxiv.org/abs/1607.07539

[42]Yi Z, Zhang H, Gong P, et al., 2017. DualGAN: unsupervised dual learning for image-to-image translation. https://arxiv.org/abs/1704.02510

[43]Yu J, 2012. Semiconductor manufacturing process monitoring using Gaussian mixture model and Bayesian method with local and nonlocal information. IEEE Trans Semicond Manuf, 25(3):480-493.

[44]Yu J, Qin S, 2008. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE J, 54(7):1811-1829.

[45]Yu J, Qin S, 2009. Multiway Gaussian mixture model based multiphase batch process monitoring. Ind Eng Chem Res, 48(18):8585-8594.

[46]Yu L, Zhang W, Wang J, et al., 2017. SeqGAN: sequence generative adversarial nets with policy gradient. 31st AAAI Conf on Artificial Intelligence, p.2852-2858.

[47]Zhao F, Feng J, Zhao J, et al., 2018. Robust LSTM-autoencoders for face de-occlusion in the wild. IEEE Trans Image Process, 27(2):778-790.

[48]Zhao J, Mathieu M, LeCun Y, 2016. Energy-based generative adversarial network. https://arxiv.org/abs/1609.03126

[49]Zhu J, Park T, Isola P, et al., 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. https://arxiv.org/abs/1703.10593

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952276/87952783; E-mail: jzus@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE