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Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies.
Support vector data description
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Weakly supervised learning for industrial optical inspection
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Auto-encoding variational bayes
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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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Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2019
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Wen-Hsuan Chu and Kris M. Kitani · 2020
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Generative adversarial networks
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Graph embedded pose clustering for anomaly detection
Amir Markovitz, Gilad Sharir, Itamar Friedman, Lihi Zelnik-Manor, and Shai Avidan · 2020
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Hyunjong Park, Jongyoun Noh, and Bumsub Ham · 2020
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Xudong Yan, Huaidong Zhang, Xuemiao Xu, Xiaowei Hu, and Pheng-Ann Heng · 2021
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Ubnormal: New benchmark for supervised open-set video anomaly detection
Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah · 2022
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Deep one-class classification via interpolated gaussian descriptor
Yuanhong Chen, Yu Tian, Guansong Pang, and Gustavo Carneiro · 2022
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Anomaly detection via reverse distillation from one-class embedding
Hanqiu Deng and Xingyu Li · 2022
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Catching both gray and black swans: Open-set supervised anomaly detection
Choubo Ding, Guansong Pang, and Chunhua Shen · 2022
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Deep semi-supervised anomaly detection
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Jakob Bozic, Domen Tabernik, and Danijel Skocaj · 2021
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Chaoqin Huang, Qinwei Xu, Yanfeng Wang, Yu Wang, and Ya Zhang · 2022
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Self-supervised predictive convolutional attentive block for anomaly detection
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Generative cooperative learning for unsupervised video anomaly detection
M Zaigham Zaheer, Arif Mahmood, M Haris Khan, Mattia Segu, Fisher Yu, and Seung-Ik Lee · 2022
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Towards open set video anomaly detection
Yuansheng Zhu, Wentao Bao, and Qi Yu · 2022
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