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Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence.
On estimation of a probability density function and mode
Emanuel Parzen · 1962
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Algorithm as 136: A k-means clustering algorithm
John A Hartigan and Manchek A Wong · 1979
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Lof: identifying density-based local outliers
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander · 2000
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Support vector method for novelty detection
Bernhard Scholkopf, Robert C Williamson, Alex J Smola, John Shawe-Taylor, and John C Platt · 2000
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A geometric framework for unsupervised anomaly detection
Eleazar Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy, and Sal Stolfo · 2002
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Uci machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
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Robust principal component analysis?
Emmanuel J Candès, Xiaodong Li, Yi Ma, and John Wright · 2011
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Principal component analysis
Ian Jolliffe · 2011
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Anomaly detection using autoencoders with nonlinear dimensionality reduction
Mayu Sakurada and Takehisa Yairi · 2014
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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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Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
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Learning representations for automatic colorization
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
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Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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ODDS library http://odds.cs.stonybrook.edu, 2016
Shebuti Rayana · 2016
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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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Towards k-means-friendly spaces: Simultaneous deep learning and clustering
Bo Yang, Xiao Fu, Nicholas D Sidiropoulos, and Mingyi Hong · 2017
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Anomaly detection with generative adversarial networks
Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, and Marius Kloft · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
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Triplet-center loss for multi-view 3d object retrieval
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
Cited alongside, same era.
Outlier detection with autoencoder ensembles
Jinghui Chen, Saket Sathe, Charu Aggarwal, and Deepak Turaga · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
Xinwei He, Yang Zhou, Zhichao Zhou, Song Bai, and Xiang Bai · 2018
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas G Dietterich · 2018
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Deep one-class classification
Lukas Ruff, Nico Gornitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert Vandermeulen, Alexander Binder, Emmanuel Müller, and Marius Kloft · 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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