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Few-shot anomaly detection (AD) is an emerging sub-field of general AD, and tries to distinguish between normal and anomalous data using only few selected samples.
Analysis and visualization of classifier performance with nonuniform class and cost distributions
Foster Provost and Tom Fawcett · 1997
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Geometric approximation via coresets
Pankaj Agarwal, Sariel Har, Peled Kasturi, and R Varadarajan · 2004
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The relationship between precision-recall and roc curves
Jesse Davis and Mark Goadrich · 2006
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Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei · 2009
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An Introduction to Statistical Learning: With Applications in R
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani · 2014
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A review of novelty detection
Marco AF Pimentel, David A Clifton, Lei Clifton, and Lionel Tarassenko · 2014
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Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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The effectiveness of data augmentation in image classification using deep learning
Luis Perez and Jason Wang · 2017
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Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?
Tiago S Nazare, Rodrigo F de Mello, and Moacir A Ponti · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, and Ian J. Goodfellow · 2018
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Learning steerable filters for rotation equivariant cnns
Maurice Weiler, Fred A. Hamprecht, and Martin Storath · 2018
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
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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
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Autoaugment: Learning augmentation strategies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2019
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Do better imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2019
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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Georg Langs, and Ursula Schmidt-Erfurth · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2019
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Fixing the train-test resolution discrepancy
Hugo Touvron, Andrea Vedaldi, Matthijs Douze, and Hervé Jégou · 2019
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Making convolutional networks shift-invariant again
Richard Zhang · 2019
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Albumentations: Fast and flexible image augmentations
Alexander Buslaev, Vladimir I. Iglovikov, Eugene Khvedchenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A. Kalinin · 2020
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Sub-image anomaly detection with deep pyramid correspondences
Niv Cohen and Yedid Hoshen · 2020
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Semi-supervised semantic segmentation with cross-consistency training
Yassine Ouali, Celine Hudelot, and Myriam Tami · 2020
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A Comparison on Data Augmentation Methods Based on Deep Learning for Audio Classification
Shengyun Wei, Shun Zou, Feifan Liao, and weimin lang · 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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Anomalib: A deep learning library for anomaly detection, 2022
Samet Akcay, Dick Ameln, Ashwin Vaidya, Barath Lakshmanan, Nilesh Ahuja, and Utku Genc · 2022
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A survey on data augmentation for text classification
Markus Bayer, Marc-André Kaufhold, and Christian Reuter · 2022
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Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization
Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger · 2022
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A program to build E(N)-equivariant steerable CNNs
Gabriele Cesa, Leon Lang, and Maurice Weiler · 2022
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Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference
Shell Xu Hu, Da Li, Jan Stühmer, Minyoung Kim, and Timothy M Hospedales · 2022
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Registration based few-shot anomaly detection
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Vitjan Zavrtanik, Matej Kristan, and Danijel Skčaj · 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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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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Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection
Pierre Gutierrez, Antoine Cordier, Thaïs Caldeira, and Théophile Sautory · 2021
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nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Fabian Isensee, Paul F. Jaeger, Simon A. A. Kohl, Jens Petersen, and Klaus H. Maier-Hein · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratlin, and Yanfeng Wang · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Fast bayesian coresets via subsampling and quasi-newton refinement
Cian Naik, Judith Rousseau, and Trevor Campbell · 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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Spot-the-difference self-supervised pre-training for anomaly detection and segmentation
Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer · 2022
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Zero-shot versus many-shot: Unsupervised texture anomaly detection
Toshimichi Aota, Lloyd Teh Tzer Tong, and Takayuki Okatani · 2023
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Glad: A global-to-local anomaly detector
Aitor Artola, Yannis Kolodziej, Jean-Michel Morel, and Thibaud Ehret · 2023
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Segment any anomaly without training via hybrid prompt regularization, 2023
Yunkang Cao, Xiaohao Xu, Chen Sun, Yuqi Cheng, Zongwei Du, Liang Gao, and Weiming Shen · 2023
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A zero-/few-shot anomaly classification and segmentation method for cvpr 2023 vand workshop challenge tracks 1&2: 1st place on zero-shot ad and 4th place on few-shot ad, 2023
Xuhai Chen, Yue Han, and Jiangning Zhang · 2023
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Exploring the importance of pretrained feature extractors for unsupervised anomaly detection and localization
Lars Heckler, Rebecca König, and Paul Bergmann · 2023
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Winclip: Zero-/few-shot anomaly classification and segmentation
Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer · 2023
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Alias-free convnets: Fractional shift invariance via polynomial activations
Hagay Michaeli, Tomer Michaeli, and Daniel Soudry · 2023
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Pushing the limits of fewshot anomaly detection in industry vision: Graphcore
Guoyang Xie, Jinbao Wang, Jiaqi Liu, Yaochu Jin, and Feng Zheng · 2023
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Im-iad: Industrial image anomaly detection benchmark in manufacturing
Guoyang Xie, Jinbao Wang, Jiaqi Liu, Jiayi Lyu, Yong Liu, Chengjie Wang, Feng Zheng, and Yaochu Jin · 2023
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$e(2)$-equivariant vision transformer
Renjun Xu, Kaifan Yang, Ke Liu, and Fengxiang He · 2023
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What makes a good data augmentation for few-shot unsupervised image anomaly detection?
Lingrui Zhang, Shuheng Zhang, Guoyang Xie, Jiaqi Liu, Hua Yan, Jinbao Wang, Feng Zheng, and Yaochu Jin · 2023
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