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Detecting anomalies in images is an important task, especially in real-time computer vision applications.
The relationship between precision-recall and roc curves
Jesse Davis and Mark Goadrich · 2006
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The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans
Samuel G Armato III, Geoffrey McLennan, Luc Bidaut, Michael F McNitt-Gray, Charles R Meyer, Anthony P Reeves, Binsheng Zhao, Denise R Aberle, Claudia I Henschke, Eric A Hoffman, et al · 2011
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Implementing Machine Vision Systems Using FPGAs
Donald Bailey · 2012
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Anomaly Detection and Localization in Crowded Scenes
Wei-Xin Li, Vijay Mahadevan, and Nuno Vasconcelos · 2013
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Abnormal Event Detection at 150 FPS in MATLAB
Cewu Lu, Jianping Shi, and Jiaya Jia · 2013
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Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Anomaly detection using autoencoders with nonlinear dimensionality reduction
Mayu Sakurada and Takehisa Yairi · 2014
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Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba · 2015
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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, et al · 2015
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Generating Images with Perceptual Similarity Metrics based on Deep Networks
Alexey Dosovitskiy and Thomas Brox · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deconvolution and checkerboard artifacts
Augustus Odena, Vincent Dumoulin, and Chris Olah · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Single-image crowd counting via multi-column convolutional neural network
Yingying Zhang, Desen Zhou, Siqin Chen, Shenghua Gao, and Yi Ma · 2016
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Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features
Spyridon Bakas, Hamed Akbari, Aristeidis Sotiras, Michel Bilello, Martin Rozycki, Justin S. Kirby, et al · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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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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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Surface defect saliency of magnetic tile
Yibin Huang, Congying Qiu, Yue Guo, Xiaonan Wang, and Kui Yuan · 2018
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Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity
Paolo Napoletano, Flavio Piccoli, and Raimondo Schettini · 2018
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Are pre-trained cnns good feature extractors for anomaly detection in surveillance videos?
Tiago S Nazare, Rodrigo F de Mello, and Moacir A Ponti · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Machine Vision Algorithms and Applications
Carsten Steger, Markus Ulrich, and Christian Wiedemann · 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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Ganomaly: Semi-supervised anomaly detection via adversarial training
Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon · 2019
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Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions
Stepan Jezek, Martin Jonak, Radim Burget, Pavel Dvorak, and Milos Skotak · 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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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 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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Deep learning for anomaly detection: A review
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel · 2021
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Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, and Nassir Navab · 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.
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
Paul Bergmann, Sindy Löwe, Michael Fauser, David Sattlegger, and Carsten Steger · 2019
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Fishyscapes: A Benchmark for Safe Semantic Segmentation in Autonomous Driving
Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, and Cesar Cadena · 2019
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Image Anomalies: A Review and Synthesis of Detection Methods
Thibaud Ehret, Axel Davy, Jean-Michel Morel, and Mauricio Delbracio · 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.
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al · 2019
Cited alongside, same era.
Same same but differnet: Semi-supervised defect detection with normalizing flows
Marco Rudolph, Bastian Wandt, and Bodo Rosenhahn · 2021
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Multiresolution knowledge distillation for anomaly detection
Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H. Rohban, and Hamid R. Rabiee · 2021
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Efficientnetv2: Smaller models and faster training
Mingxing Tan and Quoc Le · 2021
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Student-teacher feature pyramid matching for anomaly detection
Guodong Wang, Shumin Han, Errui Ding, and Di Huang · 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
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Anomalib: A deep learning library for anomaly detection
Samet Akcay, Dick Ameln, Ashwin Vaidya, Barath Lakshmanan, Nilesh Ahuja, and Utku Genc · 2022
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Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization
Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger · 2022
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The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization
Paul Bergmann, Xin Jin, David Sattlegger, and Carsten Steger · 2022
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CFLOW-AD: Real-Time Unsupervised Anomaly Detection With Localization via Conditional Normalizing Flows
Denis Gudovskiy, Shun Ishizaka, and Kazuki Kozuka · 2022
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Scaling out-of-distribution detection for real-world settings
Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Transfer learning gaussian anomaly detection by fine-tuning representations
Oliver Rippel., Arnav Chavan., Chucai Lei., and Dorit Merhof · 2022
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Towards total recall in industrial anomaly detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler · 2022
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Fully convolutional cross-scale-flows for image-based defect detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt · 2022
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A-vit: Adaptive tokens for efficient vision transformer
Hongxu Yin, Arash Vahdat, Jose M Alvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov · 2022
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Dsr–a dual subspace re-projection network for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj · 2022
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Spot-the-difference self-supervised pre-training for anomaly detection and segmentation
Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer · 2022
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Simplenet: A simple network for image anomaly detection and localization
Zhikang Liu, Yiming Zhou, Yuansheng Xu, and Zilei Wang · 2023
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Asymmetric student-teacher networks for industrial anomaly detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt · 2023
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