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We present a two-stage framework for deep one-class classification.
One-class classifier networks for target recognition applications
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Pattern recognition and machine learning
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Longin Jan Latecki, Aleksandar Lazarevic, and Dragoljub Pokrajac · 2007
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Jeremy Elson, John R Douceur, Jon Howell, and Jared Saul · 2007
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A comprehensive survey of data mining-based fraud detection research
Clifton Phua, Vincent Lee, Kate Smith, and Ross Gayler · 2010
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Spam filtering through anomaly detection
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Scikit-learn: Machine learning in Python
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Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
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Deep learning face attributes in the wild
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Pixel recurrent neural networks
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Deep structured energy based models for anomaly detection
Shuangfei Zhai, Yu Cheng, Weining Lu, and Zhongfei Zhang · 2016
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Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep kernel learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P Xing · 2016
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Likelihood ratios for out-of-distribution detection
Jie Ren, Peter J Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, and Balaji Lakshminarayanan · 2019
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
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A theoretical analysis of contrastive unsupervised representation learning
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, and Hrishikesh Khandeparkar · 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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Inverse-transform autoencoder for anomaly detection
Chaoqing Huang, Jinkun Cao, Fei Ye, Maosen Li, Ya Zhang, and Cewu Lu · 2019
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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
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos · 2017
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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, and Balaji Lakshminarayanan · 2019
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Unsupervised embedding learning via invariant and spreading instance feature
Mang Ye, Xu Zhang, Pong C Yuen, and Shih-Fu Chang · 2019
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Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord · 2019
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Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
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Xrai: Better attributions through regions
Andrei Kapishnikov, Tolga Bolukbasi, Fernanda Viégas, and Michael Terry · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker · 2019
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Attention guided anomaly localization in images
Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, and Abhijit Mahalanobis · 2019
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Density of states estimation for out-of-distribution detection
Warren R Morningstar, Cusuh Ham, Andrew G Gallagher, Balaji Lakshminarayanan, Alexander A Alemi, and Joshua V Dillon · 2020
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Why normalizing flows fail to detect out-of-distribution data
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2020
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Classification-based anomaly detection for general data
Liron Bergman 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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Tongzhou Wang and Phillip Isola · 2020
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Distribution augmentation for generative modeling
Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, and Ilya Sutskever · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Contrastive training for improved out-of-distribution detection
Jim Winkens, Rudy Bunel, Abhijit Guha Roy, Robert Stanforth, Vivek Natarajan, Joseph R Ledsam, Patricia MacWilliams, Pushmeet Kohli, Alan Karthikesalingam, Simon Kohl, et al · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Contrastive learning, multi-view redundancy, and linear models
Christopher Tosh, Akshay Krishnamurthy, and Daniel Hsu · 2020
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Explainable deep one-class classification
Philipp Liznerski, Lukas Ruff, Robert A Vandermeulen, Billy Joe Franks, Marius Kloft, and Klaus-Robert Müller · 2020
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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 · 2020
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