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Recent advances in multimodal foundation models have set new standards in few-shot anomaly detection.
Identification of outliers
Douglas M Hawkins · 1980
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Extreme value theory for risk managers
Alexander J McNeil · 1999
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A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data
Eleazar Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy, and Sal Stolfo · 2002
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Deep one-class classification
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Billion-scale similarity search with gpus
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Detecting cyber attacks using anomaly detection with explanations and expert feedback
Md Amran Siddiqui, Jack W Stokes, Christian Seifert, Evan Argyle, Robert McCann, Joshua Neil, and Justin Carroll · 2019
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Deep nearest neighbor anomaly detection
Liron Bergman, Niv Cohen, and Yedid Hoshen · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Sub-image anomaly detection with deep pyramid correspondences
Niv Cohen and Yedid Hoshen · 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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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, Jakob Uszkoreit, and Neil Houlsby · 2020
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Deep semi-supervised anomaly detection
Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft · 2020
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Patch svdd: Patch-level svdd for anomaly detection and segmentation
Jihun Yi and Sungroh Yoon · 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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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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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, Jakob Uszkoreit, and Neil Houlsby · 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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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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A unifying review of deep and shallow anomaly detection
Lukas Ruff, Jacob R Kauffmann, Robert A Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G Dietterich, and Klaus-Robert Müller · 2021
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Grounding dino: Marrying dino with grounded pre-training for open-set object detection
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al · 2023
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On diffusion modeling for anomaly detection
Victor Livernoche, Vineet Jain, Yashar Hezaveh, and Siamak Ravanbakhsh · 2023
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Anomaly detection with conditioned denoising diffusion models
Arian Mousakhan, Thomas Brox, and Jawad Tayyub · 2023
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On pixel-level performance assessment in anomaly detection
Mehdi Rafiei, Toby P Breckon, and Alexandros Iosifidis · 2023
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Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu · 2021
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Masked autoencoders are scalable vision learners
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Anomaly detection requires better representations
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Towards total recall in industrial anomaly detection
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Diffusion models for medical anomaly detection
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Image BERT pre-training with online tokenizer
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Spot-the-difference self-supervised pre-training for anomaly detection and segmentation
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João Santos, Triet Tran, and Oliver Rippel · 2023
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Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images
Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, and Gustavo Carneiro · 2023
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Pushing the limits of fewshot anomaly detection in industry vision: Graphcore
Guoyang Xie, Jinbao Wang, Jiaqi Liu, Feng Zheng, and Yaochu Jin · 2023
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Diffusionad: Denoising diffusion for anomaly detection
Hui Zhang, Zheng Wang, Zuxuan Wu, and Yu-Gang Jiang · 2023
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Featup: A model-agnostic framework for features at any resolution
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Zero-shot anomaly detection via batch normalization
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Musc: Zero-shot industrial anomaly classification and segmentation with mutual scoring of the unlabeled images
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DINOv2: Learning robust visual features without supervision
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Customizing visual-language foundation models for multi-modal anomaly detection and reasoning
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AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection
Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, and Jiming Chen · 2024
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