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Anomaly detection (AD), separating anomalies from normal data, has many applications across domains, from security to healthcare.
Probability of error of some adaptive pattern-recognition machines
Henry Scudder · 1965
Earlier work this paper cites.
Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis
Geoffrey J McLachlan · 1975
Earlier work this paper cites.
Principal component analysis
Svante Wold, Kim Esbensen, and Paul Geladi · 1987
Earlier work this paper cites.
Identifying and eliminating mislabeled training instances
Carla E Brodley, Mark A Friedl, et al · 1996
Earlier work this paper cites.
Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
Earlier work this paper cites.
Support vector method for novelty detection
Bernhard Schölkopf, Robert C Williamson, Alexander J Smola, John Shawe-Taylor, John C Platt, et al · 1999
Earlier work this paper cites.
Lof: identifying density-based local outliers
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander · 2000
Earlier work this paper cites.
Noisy replication in skewed binary classification
Sauchi Stephen Lee · 2000
Earlier work this paper cites.
Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
Earlier work this paper cites.
A multiple resampling method for learning from imbalanced data sets
Andrew Estabrooks, Taeho Jo, and Nathalie Japkowicz · 2004
Earlier work this paper cites.
Survey over image thresholding techniques and quantitative performance evaluation
Mehmet Sezgin and Bülent Sankur · 2004
Earlier work this paper cites.
Support vector data description
David MJ Tax and Robert PW Duin · 2004
Earlier work this paper cites.
UCI machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
Earlier work this paper cites.
Asirra: a captcha that exploits interest-aligned manual image categorization
Jeremy Elson, John R Douceur, Jon Howell, and Jared Saul · 2007
Earlier work this paper cites.
Outlier detection with kernel density functions
Longin Jan Latecki, Aleksandar Lazarevic, and Dragoljub Pokrajac · 2007
Earlier work this paper cites.
Generative oversampling for mining imbalanced datasets
Alexander Liu, Joydeep Ghosh, and Cheryl E Martin · 2007
Earlier work this paper cites.
Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
Earlier work this paper cites.
Learning from positive and unlabeled examples: A survey
Bangzuo Zhang and Wanli Zuo · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Gaussian mixture models
Douglas A Reynolds · 2009
Earlier work this paper cites.
Semi-supervised novelty detection
Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2010
Cited alongside, same era.
Semisupervised one-class support vector machines for classification of remote sensing data
Jordi Mũnoz-Marí, Francesca Bovolo, Luis Gómez-Chova, Lorenzo Bruzzone, and Gustavo Camp-Valls · 2010
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Robust principal component analysis?
Emmanuel J Candès, Xiaodong Li, Yi Ma, and John Wright · 2011
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A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches
Mikel Galar, Alberto Fernandez, Edurne Barrenechea, Humberto Bustince, and Francisco Herrera · 2011
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A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function
Jae Pil Hwang, Seongkeun Park, and Euntai Kim · 2011
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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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Robust anomaly detection in images using adversarial autoencoders
Laura Beggel, Michael Pfeiffer, and Bernd Bischl · 2019
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Amanda Berg, Jörgen Ahlberg, and Michael Felsberg · 2019
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Classification-based anomaly detection for general data
Liron Bergman and Yedid Hoshen · 2019
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MVTec AD–a comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2019
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Sukarna Barua, Md Monirul Islam, Xin Yao, and Kazuyuki Murase · 2012
Cited alongside, same era.
Toward supervised anomaly detection
Nico Görnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld · 2013
Cited alongside, same era.
A survey of predictive modelling under imbalanced distributions
Paula Branco, Luis Torgo, and Rita Ribeiro · 2015
Cited alongside, same era.
Learning discriminative reconstructions for unsupervised outlier removal
Yan Xia, Xudong Cao, Fang Wen, Gang Hua, and Jian Sun · 2015
Cited alongside, same era.
A hybrid semi-supervised anomaly detection model for high-dimensional data
Hongchao Song, Zhuqing Jiang, Aidong Men, and Bo Yang · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Anomaly detection with robust deep autoencoders
Chong Zhou and Randy C Paffenroth · 2017
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
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Robust subspace recovery layer for unsupervised anomaly detection
Chieh-Hsin Lai, Dongmian Zou, and Gilad Lerman · 2019
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Dividemix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven CH Hoi · 2019
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Likelihood ratios for out-of-distribution detection
Jie Ren, Peter J Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, and Balaji Lakshminarayanan · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Self-trained deep ordinal regression for end-to-end video anomaly detection
Guansong Pang, Cheng Yan, Chunhua Shen, Anton van den Hengel, and Xiao Bai · 2020
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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 · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Mist: Multiple instance self-training framework for video anomaly detection
Jia-Chang Feng, Fa-Ting Hong, and Wei-Shi Zheng · 2021
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Cutpaste: Self-supervised learning for anomaly detection and localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister · 2021
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Semi-supervised anomaly detection in dynamic communication networks
Xuying Meng, Suhang Wang, Zhimin Liang, Di Yao, Jihua Zhou, and Yujun Zhang · 2021
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Can self-training identify suspicious ugly duckling lesions?
Mohammadreza Mohseni, Jordan Yap, William Yolland, Arash Koochek, and Stella Atkins · 2021
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Learning and evaluating representations for deep one-class classification
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, and Tomas Pfister · 2021
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