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In anomaly detection (AD), one seeks to identify whether a test sample is abnormal, given a data set of normal samples.
Estimating the support of a high-dimensional distribution
Bernhard Schölkopf, John C. Platt, John C. Shawe-Taylor, Alex J. Smola, and Robert C. Williamson · 2001
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Support vector data description
David M. J. Tax and Robert P. W. Duin · 2004
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Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
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Semi-supervised novelty detection
Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2010
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Semisupervised one-class support vector machines for classification of remote sensing data
Jordi Muñoz-Marí, Francesca Bovolo, Luis Gómez-Chova, Lorenzo Bruzzone, and Gustavo Camp-Valls · 2010
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Robot motion planning , volume 124
Jean-Claude Latombe · 2012
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Toward supervised anomaly detection
Nico Görnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 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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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Semi-supervised learning with deep generative models
Diederik P. Kingma, Danilo Jimenez Rezende, Shakir Mohamed, and Max Welling · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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A review of novelty detection
Marco AF Pimentel, David A Clifton, Lei Clifton, and Lionel Tarassenko · 2014
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Variational autoencoder based anomaly detection using reconstruction probability
Jinwon An and Sungzoon Cho · 2015
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Detecting anomalous data using auto-encoders
Jerone Andrews, Edward Morton, and Lewis Griffin · 2016
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Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and Samy Bengio · 2016
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A hybrid autoencoder and density estimation model for anomaly detection
Van Loi Cao, Miguel Nicolau, and James Mcdermott · 2016
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Incorporating expert feedback into active anomaly discovery
S. Das, W. Wong, T. Dietterich, A. Fern, and A. Emmott · 2016
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High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning
Sarah M. Erfani, Sutharshan Rajasegarar, Shanika Karunasekera, and Christopher Leckie · 2016
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Learning combination of anomaly detectors for security domain
Martin Grill and Tomáš Pevný · 2016
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Ladder Variational Autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
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Odds library, 2016
Rayana Shebuti · 2016
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Echo-state conditional variational autoencoder for anomaly detection
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Su-ids: A semi-supervised and unsupervised framework for network intrusion detection
Erxue Min, Jun Long, Qiang Liu, Jianjing Cui, Zhiping Cai, and Junbo Ma · 2018
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Kitsune: An ensemble of autoencoders for online network intrusion detection
Yisroel Mirsky, Tomer Doitshman, Yuval Elovici, and Asaf Shabtai · 2018
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One-shot learning using mixture of variational autoencoders: a generalization learning approach
Decebal Constantin Mocanu and Elena Mocanu · 2018
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Deep one-class classification
Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Lucas Deecke, Shoaib A. Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
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Suwon Suh, Daniel H Chae, Hyon-Goo Kang, and Seungjin Choi · 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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Outlier detection with autoencoder ensembles
Jinghui Chen, Saket Sathe, Charu Aggarwal, and Deepak Turaga · 2017
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Incorporating feedback into tree-based anomaly detection
Shubhomoy Das, Weng-Keen Wong, Alan Fern, Thomas G. Dietterich, and Md Amran Siddiqui · 2017
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Variational inference via
Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, and David Blei · 2017
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Unsupervised and semi-supervised anomaly detection with lstm neural networks
Tolga Ergen, Ali Hassan Mirza, and Suleyman Serdar Kozat · 2017
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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
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Learning neural random fields with inclusive auxiliary generators
Yunfu Song and Zhijian Ou · 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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Deep learning for anomaly detection: A survey
Raghavendra Chalapathy and Sanjay Chawla · 2019
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Do deep generative models know what they don’t know?
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Simple and effective prevention of mode collapse in deep one-class classification, 2020
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Deep semi-supervised anomaly detection
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