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The tremendous success of generative models in recent years raises the question whether they can also be used to perform classification.
Detecting out-of-distribution inputs to deep generative models using a test for typicality
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, and Balaji Lakshminarayanan · 1906
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Relations between the statistics of natural images and the response properties of cortical cells
David J Field · 1987
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Perceptual image distortion
Patrick C Teo and David J Heeger · 1994
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Using generative models for handwritten digit recognition
Michael Revow, Christopher KI Williams, and Geoffrey E Hinton · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
Andrew Y. Ng and Michael I. Jordan · 2001
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Classification with hybrid generative/discriminative models
Rajat Raina, Yirong Shen, Andrew Y. Ng, and Andrew McCallum · 2003
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The tradeoff between generative and discriminative classifiers
Guillaume Bouchard and Bill Triggs · 2004
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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A hybrid generative/discriminative approach to semi-supervised classifier design
Akinori Fujino, Naonori Ueda, and Kazumi Saito · 2005
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An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators
Percy Liang and Michael I. Jordan · 2008
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Reading digits in natural images with unsupervised feature learning, 2011
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Multi-prediction deep boltzmann machines
Ian J. Goodfellow, Mehdi Mirza, Aaron C. Courville, and Yoshua Bengio · 2013
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Machine learning : a probabilistic perspective
Kevin P. Murphy · 2013
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Plug-and-play priors for model based reconstruction
Singanallur V Venkatakrishnan, Charles A Bouman, and Brendt Wohlberg · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Perceptual image quality assessment using a normalized laplacian pyramid
Valero Laparra, Johannes Ballé, Alexander Berardino, and Eero P Simoncelli · 2016
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Resnet in resnet: Generalizing residual architectures
Sasha Targ, Diogo Almeida, and Kevin Lyman · 2016
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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2016
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Cited alongside, same era.
Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2020
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Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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E-abs: extending the analysis-by-synthesis robust classification model to more complex image domains
An Ju and David Wagner · 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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Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
Cited alongside, same era.
Adversarial examples for generative models
Jernej Kos, Ian Fischer, and Dawn Song · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas G. Dietterich · 2019
Cited alongside, same era.
Are generative classifiers more robust to adversarial attacks?
Yingzhen Li, John Bradshaw, and Yash Sharma · 2019
Cited alongside, same era.
Do deep generative models know what they don’t know?
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, and Balaji Lakshminarayanan · 2019
Cited alongside, same era.
Towards the first adversarially robust neural network model on MNIST
Lukas Schott, Jonas Rauber, Matthias Bethge, and Wieland Brendel · 2019
Cited alongside, same era.
Zahra Kadkhodaie and Eero P Simoncelli · 2020
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Selectivity and robustness of sparse coding networks
Dylan M Paiton, Charles G Frye, Sheng Y Lundquist, Joel D Bowen, Ryan Zarcone, and Bruno A Olshausen · 2020
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Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax
Jonas Rauber, Roland Zimmermann, Matthias Bethge, and Wieland Brendel · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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Defending against image corruptions through adversarial augmentations
Dan A Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, Andras Gyorgy, Timothy Mann, and Sven Gowal · 2021
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Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Generative classifiers as a basis for trustworthy image classification
Radek Mackowiak, Lynton Ardizzone, Ullrich Kothe, and Carsten Rother · 2021
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Improved denoising diffusion probabilistic models
Alex Nichol and Prafulla Dhariwal · 2021
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Fixing data augmentation to improve adversarial robustness
Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A Calian, Florian Stimberg, Olivia Wiles, and Timothy Mann · 2021
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Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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Adversarial purification with score-based generative models
Jongmin Yoon, Sung Ju Hwang, and Juho Lee · 2021
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