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Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images.
“Same same but differnet: Semi-supervised defect detection with normalizing flows,”
Marco Rudolph, Bastian Wandt, and Bodo Rosenhahn, · 1916
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“Geometric approximation via coresets,”
Pankaj K Agarwal, Sariel Har-Peled, Kasturi R Varadarajan, et al., · 2005
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“Visualizing data using t-sne.,”
Laurens Van der Maaten and Geoffrey Hinton, · 2008
Earlier work this paper cites.
“Anomaly detection: A survey,”
Varun Chandola, Arindam Banerjee, and Vipin Kumar, · 2009
Earlier work this paper cites.
“Anomaly detection using autoencoders with nonlinear dimensionality reduction,”
Mayu Sakurada and Takehisa Yairi, · 2014
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“Wide residual networks,”
Sergey Zagoruyko and Nikos Komodakis, · 2016
Earlier work this paper cites.
“Imagenet classification with deep convolutional neural networks,”
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, · 2017
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“Active learning for convolutional neural networks: A core-set approach,”
Ozan Sener and Silvio Savarese, · 2017
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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.
“Anomaly detection neural network with dual auto-encoders gan and its industrial inspection applications,”
Ta-Wei Tang, Wei-Han Kuo, Jauh-Hsiang Lan, Chien-Fang Ding, Hakiem Hsu, and Hong-Tsu Young, · 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.
“Sub-image anomaly detection with deep pyramid correspondences,”
Niv Cohen and Yedid Hoshen, · 2020
Cited alongside, same era.
“Towards visually explaining variational autoencoders,”
Wenqian Liu, Runze Li, Meng Zheng, Srikrishna Karanam, Ziyan Wu, Bir Bhanu, Richard J Radke, and Octavia Camps, · 2020
Cited alongside, same era.
“Trustmae: A noise-resilient defect classification framework using memory-augmented auto-encoders with trust regions,”
Daniel Stanley Tan, Yi-Chun Chen, Trista Pei-Chun Chen, and Wei-Chao Chen, · 2021
Later among the works it cites.
“Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection,”
Jinlei Hou, Yingying Zhang, Qiaoyong Zhong, Di Xie, Shiliang Pu, and Hong Zhou, · 2021
Later among the works it cites.
“Student-teacher feature pyramid matching for unsupervised anomaly detection,”
Guodong Wang, Shumin Han, Errui Ding, and Di Huang, · 2021
Later among the works it cites.
“Inpainting transformer for anomaly detection,”
Jonathan Pirnay and Keng Chai, · 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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“Small-gan: Speeding up gan training using core-sets,”
Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, and Augustus Odena, · 2020
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.
“Deep learning for unsupervised anomaly localization in industrial images: A survey,”
Xian Tao, Xinyi Gong, Xin Zhang, Shaohua Yan, and Chandranath Adak, · 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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