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Deep anomaly detection methods learn representations that separate between normal and anomalous images.
Support vector method for novelty detection
Scholkopf, B.; Williamson, R. C.; Smola, A. J.; Shawe-Taylor, J.; and Platt, J. C. 2000 · 2000
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A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020a · 2002
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A geometric framework for unsupervised anomaly detection
Eskin, E.; Arnold, A.; Prerau, M.; Portnoy, L.; and Stolfo, S. 2002 · 2002
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Improved baselines with momentum contrastive learning
Chen, X.; Fan, H.; Girshick, R.; and He, K. 2020b · 2003
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Support vector data description
Tax, D. M.; and Duin, R. P. 2004 · 2004
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Rethinking assumptions in deep anomaly detection
Ruff, L.; Vandermeulen, R. A.; Franks, B. J.; Müller, K.-R.; and Kloft, M. 2020 · 2006
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Asirra: a CAPTCHA that exploits interest-aligned manual image categorization
Elson, J.; Douceur, J. R.; Howell, J.; and Saul, J. 2007 · 2007
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Outlier detection with kernel density functions
Latecki, L. J.; Lazarevic, A.; and Pokrajac, D. 2007 · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Principal component analysis
Jolliffe, I. 2011 · 2011
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Learning and Evaluating Representations for Deep One-class Classification
Sohn, K.; Li, C.-L.; Yoon, J.; Jin, M.; and Pfister, T. 2020 · 2011
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Ensemble Gaussian mixture models for probability density estimation
Glodek, M.; Schels, M.; and Schwenker, F. 2013 · 2013
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Learning representations for automatic colorization
Larsson, G.; Maire, M.; and Shakhnarovich, G. 2016 · 2016
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Deep multi-scale video prediction beyond mean square error
Mathieu, M.; Couprie, C.; and LeCun, Y. 2016 · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M.; and Favaro, P. 2016 · 2016
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Colorful image colorization
Zhang, R.; Isola, P.; and Efros, A. A. 2016 · 2016
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J.; Pascanu, R.; Rabinowitz, N.; Veness, J.; Desjardins, G.; Rusu, A. A.; Milan, K.; Quan, J.; Ramalho, T.; Grabska-Barwinska, A.; et al. 2017 · 2017
Using self-supervised learning can improve model robustness and uncertainty
Hendrycks, D.; Mazeika, M.; Kadavath, S.; and Song, D. 2019 · 2019
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Learning deep features for one-class classification
Perera, P.; and Patel, V. M. 2019 · 2019
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Detecting semantic anomalies
Ahmed, F.; and Courville, A. 2020 · 2020
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Classification-Based Anomaly Detection for General Data
Bergman, L.; and Hoshen, Y. 2020 · 2020
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Object detection in optical remote sensing images: A survey and a new benchmark
Li, K.; Wan, G.; Cheng, G.; Meng, L.; and Han, J. 2020 · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
Tack, J.; Mo, S.; Jeong, J.; and Shin, J. 2020 · 2020
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Unsupervised representation learning by predicting image rotations
Gidaris, S.; Singh, P.; and Komodakis, N. 2018 · 2018
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Deep Anomaly Detection Using Geometric Transformations
Golan, I.; and El-Yaniv, R. 2018 · 2018
Cited alongside, same era.
Deep one-class classification
Ruff, L.; Gornitz, N.; Deecke, L.; Siddiqui, S. A.; Vandermeulen, R.; Binder, A.; Müller, E.; and Kloft, M. 2018 · 2018
Cited alongside, same era.
MVTec AD–A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
Bergmann, P.; Fauser, M.; Sattlegger, D.; and Steger, C. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Wang, T.; and Isola, P. 2020 · 2020
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Transfer-based semantic anomaly detection
Deecke, L.; Ruff, L.; Vandermeulen, R. A.; and Bilen, H. 2021 · 2021
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PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Reiss, T.; Cohen, N.; Bergman, L.; and Hoshen, Y. 2021 · 2021
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Understanding the behaviour of contrastive loss
Wang, F.; and Liu, H. 2021 · 2021
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