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Anomaly detection is a well-established research area that seeks to identify samples outside of a predetermined distribution.
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.
A geometric framework for unsupervised anomaly detection
Eleazar Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy, and Sal Stolfo · 2002
Earlier work this paper cites.
Support vector data description
David MJ Tax and Robert PW Duin · 2004
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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K-nearest neighbor
Leif E Peterson · 2009
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Ensemble gaussian mixture models for probability density estimation
Michael Glodek, Martin Schels, and Friedhelm Schwenker · 2013
Earlier work this paper cites.
Learning discriminative reconstructions for unsupervised outlier removal
Yan Xia, Xudong Cao, Fang Wen, Gang Hua, and Jian Sun · 2015
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
Earlier work this paper cites.
BCCD Dataset, Oct. 2021
shenggan · 2017
Earlier work this paper cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Earlier work this paper cites.
Multi-class Weather Dataset for Image Classification
Gbeminiyi Ajayi · 2018
Earlier work this paper cites.
Image anomaly detection with generative adversarial networks
Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, and Marius Kloft · 2018
Earlier work this paper cites.
Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
Earlier work this paper cites.
Anomaly detection with generative adversarial networks for multivariate time series
Dan Li, Dacheng Chen, Jonathan Goh, and See-kiong Ng · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R. Srikant · 2018
Earlier work this paper cites.
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.
Out-of-distribution detection using an ensemble of self supervised leave-out classifiers
Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu, Dipankar Das, Bharat Kaul, and Theodore L Willke · 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.
Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2019
Cited alongside, same era.
Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
Cited alongside, same era.
Puzzle-ae: Novelty detection in images through solving puzzles
Mohammadreza Salehi, Ainaz Eftekhar, Niousha Sadjadi, Mohammad Hossein Rohban, and Hamid R Rabiee · 2020
Later among the works it cites.
Csi: Novelty detection via contrastive learning on distributionally shifted instances
Jihoon Tack, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin · 2020
Later among the works it cites.
A simple and effective baseline for out-of-distribution detection using abstention
Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel, Gopinath Chennupati, Tanmoy Bhattacharya, and Jeff Bilmes · 2020
Later among the works it cites.
Attention guided anomaly localization in images
Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, and Abhijit Mahalanobis · 2020
Later among the works it cites.
Transfer-based semantic anomaly detection
Lucas Deecke, Lukas Ruff, Robert A Vandermeulen, and Hakan Bilen · 2021
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
Cited alongside, same era.
Concrete Crack Images for Classification
Çağlar Fırat Özgenel · 2019
Cited alongside, same era.
Detecting semantic anomalies
Faruk Ahmed and Aaron Courville · 2020
Cited alongside, same era.
Deep nearest neighbor anomaly detection
Liron Bergman, Niv Cohen, and Yedid Hoshen · 2020
Cited alongside, same era.
Classification-based anomaly detection for general data
Liron Bergman and Yedid Hoshen · 2020
Cited alongside, same era.
Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 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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VT-ADL: A vision transformer network for image anomaly detection and localization
Pankaj Mishra, Riccardo Verk, Daniele Fornasier, Claudio Piciarelli, and Gian Luca Foresti · 2021
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Inpainting transformer for anomaly detection
Jonathan Pirnay and Keng Chai · 2021
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Panda: Adapting pretrained features for anomaly detection and segmentation
Tal Reiss, Niv Cohen, Liron Bergman, and Yedid Hoshen · 2021
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Mean-shifted contrastive loss for anomaly detection
Tal Reiss and Yedid Hoshen · 2021
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Imagenet-21k pretraining for the masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 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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Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks
Lin Wang and Kuk-Jin Yoon · 2021
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Do we really need to learn representations from in-domain data for outlier detection?
Zhisheng Xiao, Qing Yan, and Yali Amit · 2021
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