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Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection.
Pyramid methods in image processing
Edward H Adelson, Charles H Anderson, James R Bergen, Peter J Burt, and Joan M Ogden · 1984
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Weakly supervised learning for industrial optical inspection
Matthias Wieler and Tobias Hahn · 2007
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 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, et al · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 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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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Improving unsupervised defect segmentation by applying structural similarity to autoencoders
Paul Bergmann, Sindy Löwe, Michael Fauser, David Sattlegger, and Carsten Steger · 2018
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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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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2020
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Padim: a patch distribution modeling framework for anomaly detection and localization
Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier · 2020
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Iterative energy-based projection on a normal data manifold for anomaly localization
David Dehaene, Oriel Frigo, Sébastien Combrexelle, and Pierre Eline · 2020
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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, et al · 2020
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An automatic analog instrument reading system using computer vision and inspection robot
Jian Huang, Junzhe Wang, Yihua Tan, Dongrui Wu, and Yu Cao · 2020
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Deep generative model using unregularized score for anomaly detection with heterogeneous complexity
Takashi Matsubara, Kazuki Sato, Kenta Hama, Ryosuke Tachibana, and Kuniaki Uehara · 2020
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Segmentation-based deep-learning approach for surface-defect detection
Domen Tabernik, Samo Šela, Jure Skvarč, and Danijel Skočaj · 2020
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Dfr: Deep feature reconstruction for unsupervised anomaly segmentation
Jie Yang, Yong Shi, and Zhiquan Qi · 2020
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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 · 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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Wafersegclassnet-a light-weight network for classification and segmentation of semiconductor wafer defects
Subhrajit Nag, Dhruv Makwana, Sparsh Mittal, C Krishna Mohan, et al · 2022
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Flexible robot-based in-line measurement system for high-precision optical surface inspection
Christopher Naverschnigg, Ernst Csencsics, and Georg Schitter · 2022
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Inpainting transformer for anomaly detection
Jonathan Pirnay and Keng Chai · 2022
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Detection of surface defects on pharmaceutical solid oral dosage forms with convolutional neural networks
Domen Rački, Dejan Tomaževič, and Danijel Skočaj · 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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Tiny defect detection in high-resolution aero-engine blade images via a coarse-to-fine framework
Dawei Li, Yida Li, Qian Xie, Yuxiang Wu, Zhenghao Yu, and Jun Wang · 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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Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows
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Draem-a discriminatively trained reconstruction embedding for surface anomaly detection
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Reconstruction by inpainting for visual anomaly detection
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Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization
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A survey on unsupervised industrial anomaly detection algorithms
Yajie Cui, Zhaoxiang Liu, and Shiguo Lian · 2022
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Hannah M Schlüter, Jeremy Tan, Benjamin Hou, and Bernhard Kainz · 2022
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Deep learning for unsupervised anomaly localization in industrial images: A survey
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Review of damage mechanism and protection of aero-engine blades based on impact properties
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A unified model for multi-class anomaly detection
Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, and Xinyi Le · 2022
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Nico++: Towards better benchmarking for domain generalization
Xingxuan Zhang, Linjun Zhou, Renzhe Xu, Peng Cui, Zheyan Shen, and Haoxin Liu · 2022
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Ood-cv: A benchmark for robustness to out-of-distribution shifts of individual nuisances in natural images
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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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Steerable equivariant representation learning
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Few-shot semantic segmentation for industrial defect recognition
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Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection
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