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Unsupervised anomaly detection and localization, as of one the most practical and challenging problems in computer vision, has received great attention in recent years.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identifying and categorizing anomalies in retinal imaging data
Philipp Seeböck, Sebastian Waldstein, Sophie Klimscha, Bianca S Gerendas, René Donner, Thomas Schlegl, Ursula Schmidt-Erfurth, and Georg Langs · 2016
Earlier work this paper cites.
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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Ganomaly: Semi-supervised anomaly detection via adversarial training
Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon · 2018
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Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Generative probabilistic novelty detection with adversarial autoencoders
Stanislav Pidhorskyi, Ranya Almohsen, and Gianfranco Doretto · 2018
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Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
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Adversarially learned one-class classifier for novelty detection
Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, and Ehsan Adeli · 2018
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Efficient gan-based anomaly detection
Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar · 2018
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Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection
Samet Akçay, Amir Atapour-Abarghouei, and Toby P Breckon · 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
Cited alongside, same era.
Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel · 2019
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.
Sub-image anomaly detection with deep pyramid correspondences
Niv Cohen and Yedid Hoshen · 2020
Cited alongside, same era.
Panda: Adapting pretrained features for anomaly detection and segmentation
Tal Reiss, Niv Cohen, Liron Bergman, and Yedid Hoshen · 2021
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Modeling the distribution of normal data in pre-trained deep features for anomaly detection
Oliver Rippel, Patrick Mertens, and Dorit Merhof · 2021
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Towards total recall in industrial anomaly detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler · 2021
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Same same but differnet: Semi-supervised defect detection with normalizing flows
Marco Rudolph, Bastian Wandt, and Bodo Rosenhahn · 2021
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Hannah M Schlüter, Jeremy Tan, Benjamin Hou, and Bernhard Kainz · 2021
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Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Dfr: Deep feature reconstruction for unsupervised anomaly segmentation
Jie Yang, Yong Shi, and Zhiquan Qi · 2020
Cited alongside, same era.
Patch svdd: Patch-level svdd for anomaly detection and segmentation
Jihun Yi and Sungroh Yoon · 2020
Cited alongside, same era.
The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization
Paul Bergmann, Xin Jin, David Sattlegger, and Carsten Steger · 2021
Cited alongside, same era.
Padim: a patch distribution modeling framework for anomaly detection and localization
Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier · 2021
Cited alongside, same era.
Denis Gudovskiy, Shun Ishizaka, and Kazuki Kozuka · 2021
Cited alongside, same era.
Cutpaste: Self-supervised learning for anomaly detection and localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister · 2021
Cited alongside, same era.
Later among the works it cites.
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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Glancing at the patch: Anomaly localization with global and local feature comparison
Shenzhi Wang, Liwei Wu, Lei Cui, and Yujun Shen · 2021
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Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows
Jiawei Yu, Ye Zheng, Xiang Wang, Wei Li, Yushuang Wu, Rui Zhao, and Liwei Wu · 2021
Later among the works it cites.
Draem-a discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj · 2021
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Ye Zheng, Xiang Wang, Rui Deng, Tianpeng Bao, Rui Zhao, and Liwei Wu · 2021
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Anomaly detection via reverse distillation from one-class embedding
Hanqiu Deng and Xingyu Li · 2022
Closest in time.
Fully convolutional cross-scale-flows for image-based defect detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt · 2022
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