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Anomaly detection is a crucial task across different domains and data types.
Robust real-time unusual event detection using multiple fixed-location monitors
Amit Adam, Ehud Rivlin, Ilan Shimshoni, and Daviv Reinitz · 2008
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Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
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Anomaly detection in crowded scenes
Vijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos · 2010
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Anomalous behaviour detection using spatiotemporal oriented energies, subset inclusion histogram comparison and event-driven processing
Andrei Zaharescu and Richard Wildes · 2010
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Online detection of unusual events in videos via dynamic sparse coding
Bin Zhao, Li Fei-Fei, and Eric P. Xing · 2011
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Abnormal event detection at 150 fps in matlab
Cewu Lu, Jianping Shi, and Jiaya Jia · 2013
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Anomaly detection using autoencoders with nonlinear dimensionality reduction
Mayu Sakurada and Takehisa Yairi · 2014
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Enhanced anomaly detection via pls regression models and information entropy theory
Harrou Fouzi and Ying Sun · 2015
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Learning temporal regularity in video sequences
Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K Roy-Chowdhury, and Larry S Davis · 2016
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Sparse gaussian markov random field mixtures for anomaly detection
Tsuyoshi Idé, Ankush Khandelwal, and Jayant Kalagnanam · 2016
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Anomaly detection in video using predictive convolutional long short-term memory networks
Jefferson Ryan Medel and Andreas Savakis · 2016
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Fast anomaly detection in traffic surveillance video based on robust sparse optical flow
Hanlin Tan, Yongping Zhai, Yu Liu, and Maojun Zhang · 2016
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Abnormal event detection in videos using spatiotemporal autoencoder
Yong Shean Chong and Yong Haur Tay · 2017
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Deeplog: Anomaly detection and diagnosis from system logs through deep learning
Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar · 2017
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Remembering history with convolutional lstm for anomaly detection
Weixin Luo, Wen Liu, and Shenghua Gao · 2017
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Remembering history with convolutional lstm for anomaly detection
Weixin Luo, Wen Liu, and Shenghua Gao · 2017
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A revisit of sparse coding based anomaly detection in stacked rnn framework
Weixin Luo, Wen Liu, and Shenghua Gao · 2017
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Abnormal event detection in videos using generative adversarial nets
Mahdyar Ravanbakhsh, Moin Nabi, Enver Sangineto, Lucio Marcenaro, Carlo Regazzoni, and Nicu Sebe · 2017
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Ganomaly: Semi-supervised anomaly detection via adversarial training
Samet Akçay, Amir Atapour-Abarghouei, and T. Breckon · 2018
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Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)
Noel CF Codella, David Gutman, M Emre Celebi, Brian Helba, Michael A Marchetti, Stephen W Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, et al · 2018
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Identifying medical diagnoses and treatable diseases by image-based deep learning
Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina CS Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, et al · 2018
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Head ct - hemorrhage, 2018
Felipe Campos Kitamura · 2018
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Real-world anomaly detection in surveillance videos
Waqas Sultani, Chen Chen, and Mubarak Shah · 2018
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Real-world anomaly detection in surveillance videos
Waqas Sultani, Chen Chen, and Mubarak Shah · 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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Brain mri images for brain tumor detection, 2019
Navoneel Chakrabarty · 2019
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Deep learning for anomaly detection: A survey
Raghavendra Chalapathy and Sanjay Chawla · 2019
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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
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Robust anomaly detection for multivariate time series through stochastic recurrent neural network
Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei · 2019
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A review on outlier/anomaly detection in time series data
Ane Bl’azquez-Garc’ia, Angel Conde, Usue Mori, and José Antonio Lozano · 2020
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Hyperkvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
Hanna Borgli, Vajira Thambawita, Pia H Smedsrud, Steven Hicks, Debesh Jha, Sigrun L Eskeland, Kristin Ranheim Randel, Konstantin Pogorelov, Mathias Lux, Duc Tien Dang Nguyen, et al · 2020
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Unsupervised lesion detection via image restoration with a normative prior
Xiaoran Chen, Suhang You, Kerem Can Tezcan, and Ender Konukoglu · 2020
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Robusttad: Robust time series anomaly detection via decomposition and convolutional neural networks
Jingkun Gao, Xiaomin Song, Qingsong Wen, Pichao Wang, Liang Sun, and Huan Xu · 2020
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Deep learning for anomaly detection
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton van den Hengel · 2020
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Learning memory-guided normality for anomaly detection
Hyunjong Park, Jongyoun Noh, and Bumsub Ham · 2020
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Learning and evaluating representations for deep one-class classification
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, and Tomas Pfister · 2020
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Attention guided anomaly localization in images
Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, and Abhijit Mahalanobis · 2020
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Covid-19 screening on chest x-ray images using deep learning based anomaly detection
Jianpeng Zhang, Yutong Xie, Yi Li, Chunhua Shen, and Yong Xia · 2020
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Multivariate time-series anomaly detection via graph attention network
Hang Zhao, Yujing Wang, Juanyong Duan, Congrui Huang, Defu Cao, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, and Qi Zhang · 2020
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The MVTec anomaly detection dataset: A comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger · 2021
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Padim: a patch distribution modeling framework for anomaly detection and localization
Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier · 2021
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Deep learning for medical anomaly detection–a survey
Tharindu Fernando, Harshala Gammulle, Simon Denman, Sridha Sridharan, and Clinton Fookes · 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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Panda: Adapting pretrained features for anomaly detection and segmentation
Tal Reiss, Niv Cohen, Liron Bergman, and Yedid Hoshen · 2021
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Easynet: An easy network for 3d industrial anomaly detection
Rui Chen, Guoyang Xie, Jiaqi Liu, Jinbao Wang, Ziqi Luo, Jinfan Wang, and Feng Zheng · 2023
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Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi · 2023
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A survey of methods for automated quality control based on images
Jan Diers and Christian Pigorsch · 2023
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Multimodal-gpt: A vision and language model for dialogue with humans, 2023
Tao Gong, Chengqi Lyu, Shilong Zhang, Yudong Wang, Miao Zheng, Qian Zhao, Kuikun Liu, Wenwei Zhang, Ping Luo, and Kai Chen · 2023
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Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Gregoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, and Klaus-Robert Muller · 2021
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Constrained contrastive distribution learning for unsupervised anomaly detection and localisation in medical images
Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W Verjans, Rajvinder Singh, and Gustavo Carneiro · 2021
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Student-teacher feature pyramid matching for anomaly detection
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Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization
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The MVTec 3d-AD dataset for unsupervised 3d anomaly detection and localization
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Anomaly detection in 3d point clouds using deep geometric descriptors
Paul Bergmann and David Sattlegger · 2022
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Semi-supervised knowledge distillation for tiny defect detection
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Winclip: Zero-/few-shot anomaly classification and segmentation
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A masked reverse knowledge distillation method incorporating global and local information for image anomaly detection
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Segment anything
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Component-aware anomaly detection framework for adjustable and logical industrial visual inspection
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