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The goal of anomaly detection is to identify anomalous samples from normal ones.
Estimating the support of a high-dimensional distribution
Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson · 2001
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Support vector data description
David MJ Tax and Robert PW Duin · 2004
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Anomaly-based network intrusion detection: Techniques, systems and challenges
Pedro Garcia-Teodoro, Jesus Diaz-Verdejo, Gabriel Maciá-Fernández, and Enrique Vázquez · 2009
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Toward supervised anomaly detection
Nico Görnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Variational autoencoder based anomaly detection using reconstruction probability
Jinwon An and Sungzoon Cho · 2015
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Learning discriminative reconstructions for unsupervised outlier removal
Yan Xia, Xudong Cao, Fang Wen, Gang Hua, and Jian Sun · 2015
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Fraud detection system: A survey
Aisha Abdallah, Mohd Aizaini Maarof, and Anazida Zainal · 2016
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Deep structured energy based models for anomaly detection
Shuangfei Zhai, Yu Cheng, Weining Lu, and Zhongfei Zhang · 2016
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Adversarial feature learning
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2017
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, and Aaron C. Courville · 2017
Cited alongside, same era.
Alice: Towards understanding adversarial learning for joint distribution matching
Chunyuan Li, Hao Liu, Changyou Chen, Yuchen Pu, Liqun Chen, Ricardo Henao, and Lawrence Carin · 2017
Cited alongside, same era.
Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley · 2017
Cited alongside, same era.
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
Adversarially learned anomaly detection
Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, and Vijay Chandrasekhar · 2018
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen · 2018
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Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel · 2019
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Ocgan: One-class novelty detection using gans with constrained latent representations
Pramuditha Perera, Ramesh Nallapati, and Bing Xiang · 2019
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Deep semi-supervised anomaly detection
Lukas Ruff, Robert A Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft · 2019
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Cited alongside, same era.
Ganomaly: Semi-supervised anomaly detection via adversarial training
Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon · 2018
Cited alongside, same era.
Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2018
Cited alongside, same era.
Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
Cited alongside, same era.
Efficient gan-based anomaly detection
Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar · 2018
Cited alongside, same era.
Discovering anomalies by incorporating feedback from an expert
Shubhomoy Das, Weng-Keen Wong, Thomas Dietterich, Alan Fern, and Andrew Emmott · 2020
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Negative data augmentation
Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, and Stefano Ermon · 2020
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Deep learning for medical anomaly detection–a survey
Tharindu Fernando, Harshala Gammulle, Simon Denman, Sridha Sridharan, and Clinton Fookes · 2021
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Deep learning for anomaly detection: A review
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel · 2021
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Toward deep supervised anomaly detection: Reinforcement learning from partially labeled anomaly data
Guansong Pang, Anton van den Hengel, Chunhua Shen, and Longbing Cao · 2021
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