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Anomaly detection, the task of identifying unusual samples in data, often relies on a large set of training samples.
One-class classifier networks for target recognition applications
Mary M Moya, Mark W Koch, and Larry D Hostetler · 1993
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Support vector method for novelty detection
Bernhard Schölkopf, Robert C Williamson, Alex Smola, John Shawe-Taylor, and John Platt · 1999
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Knowledge transfer in learning to recognize visual objects classes
Li Fei-Fei · 2006
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One-shot learning of object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2006
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Lost in quantization: Improving particular object retrieval in large scale image databases
James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman · 2008
Earlier work this paper cites.
Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Outlier analysis
Charu C Aggarwal · 2015
Earlier work this paper cites.
Variational autoencoder based anomaly detection using reconstruction probability
Jinwon An and Sungzoon Cho · 2015
Earlier work this paper cites.
A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure
Christian Koch, Kristina Georgieva, Varun Kasireddy, Burcu Akinci, and Paul Fieguth · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
Precomputed real-time texture synthesis with markovian generative adversarial networks
Chuan Li and Michael Wand · 2016
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Deep structured energy based models for anomaly detection
Shuangfei Zhai, Yu Cheng, Weining Lu, and Zhongfei Zhang · 2016
Earlier work this paper cites.
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Cited alongside, same era.
Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
Cited alongside, same era.
Clear: Cumulative learning for one-shot one-class image recognition
Jedrzej Kozerawski and Matthew Turk · 2018
Cited alongside, same era.
Singan: Learning a generative model from a single natural image
Tamar Rott Shaham, Tali Dekel, and Tomer Michaeli · 2019
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Ingan: Capturing and retargeting the ”dna” of a natural image
Assaf Shocher, Shai Bagon, Phillip Isola, and Michal Irani · 2019
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Classification-based anomaly detection for general data
Liron Bergman and Yedid Hoshen · 2020
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Few-shot one-class classification via meta-learning
Ahmed Frikha, Denis Krompaß, Hans-Georg Köpken, and Volker Tresp · 2020
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Drocc: Deep robust one-class classification
Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, and Prateek Jain · 2020
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Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2018
Cited alongside, same era.
Learning representations of ultrahigh-dimensional data for random distance-based outlier detection
Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu · 2018
Cited alongside, same era.
Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
Cited alongside, same era.
Non-stationary texture synthesis by adversarial expansion
Yang Zhou, Zhen Zhu, Xiang Bai, Dani Lischinski, Daniel Cohen-Or, and Hui Huang · 2018
Cited alongside, same era.
Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2019
Cited alongside, same era.
A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
Cited alongside, same era.
Anna Kruspe · 2019
Cited alongside, same era.
Hierarchical patch vae-gan: Generating diverse videos from a single sample
Shir Gur, Sagie Benaim, and Lior Wolf · 2020
Later among the works it cites.
Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
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Deep reinforcement learning for unknown anomaly detection, 2020
Guansong Pang, Anton van den Hengel, Chunhua Shen, and Longbing Cao · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola · 2020
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Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni · 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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Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
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Same same but differnet: Semi-supervised defect detection with normalizing flows
Marco Rudolph, Bastian Wandt, and Bodo Rosenhahn · 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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