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Anomaly detection methods have demonstrated remarkable success across various applications.
“Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,”
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger, · 2019
Earlier work this paper cites.
“Bracol–a brazilian arabica coffee leaf images dataset to identification and quantification of coffee diseases and pests,”
Renato A Krohling, José Esgario, and José A Ventura, · 2019
Earlier work this paper cites.
“Probabilistic modeling of deep features for out-of-distribution and adversarial detection,”
Nilesh A Ahuja, Ibrahima Ndiour, Trushant Kalyanpur, and Omesh Tickoo, · 2019
Earlier work this paper cites.
“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
Earlier work this paper cites.
“Mixed supervision for surface-defect detection: From weakly to fully supervised learning,”
Jakob Božič, Domen Tabernik, and Danijel Skočaj, · 2021
Earlier work this paper cites.
“Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,”
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj, · 2021
Earlier work this paper cites.
“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
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.
“Student-teacher feature pyramid matching for anomaly detection,”
Guodong Wang, Shumin Han, Errui Ding, and Di Huang, · 2021
Cited alongside, same era.
“Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows,”
Denis Gudovskiy, Shun Ishizaka, and Kazuki Kozuka, · 2022
Cited alongside, same era.
“Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization,”
Sungwook Lee, Seunghyun Lee, and Byung Cheol Song, · 2022
“Towards total recall in industrial anomaly detection,”
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler, · 2022
Later among the works it cites.
“Anomaly detection via reverse distillation from one-class embedding,”
Hanqiu Deng and Xingyu Li, · 2022
Later among the works it cites.
“Recognition of defective mineral wool using pruned resnet models,”
Mehdi Rafiei, Dat Thanh Tran, and Alexandros Iosifidis, · 2023
Closest in time.
“Efficientad: Accurate visual anomaly detection at millisecond-level latencies,”
Kilian Batzner, Lars Heckler, and Rebecca König, · 2023
Closest in time.
“Computer vision on x-ray data in industrial production and security applications: A comprehensive survey,”
Mehdi Rafiei, Jenni Raitoharju, and Alexandros Iosifidis, · 2023
Closest in time.
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Cited alongside, same era.
“Csflow: Learning optical flow via cross strip correlation for autonomous driving,”
Hao Shi, Yifan Zhou, Kailun Yang, Xiaoting Yin, and Kaiwei Wang, · 2022
Cited alongside, same era.