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Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Semi-supervised learning with deep generative models
Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
Earlier work this paper cites.
NICE: non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 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.
Conditional image generation with pixelcnn decoders
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
Earlier work this paper cites.
Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Earlier work this paper cites.
Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
Cited alongside, same era.
Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal S. Mian · 2018
Cited alongside, same era.
Why do deep convolutional networks generalize so poorly to small image transformations?
Aharon Azulay and Yair Weiss · 2018
Cited alongside, same era.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2018
Cited alongside, same era.
Darccc: Detecting adversaries by reconstruction from class conditional capsules
Nicholas Frosst, Sara Sabour, and Geoffrey Hinton · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Later among the works it cites.
Are generative classifiers more robust to adversarial attacks?
Yingzhen Li, John Bradshaw, and Yash Sharma · 2018
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Amir Rosenfeld, Richard S. Zemel, and John K. Tsotsos · 2018
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2018
Later among the works it cites.
Invertible residual networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, and Jörn-Henrik Jacobsen · 2019
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Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, and Ian J. Goodfellow · 2018
Cited alongside, same era.
Flow-gan: Combining maximum likelihood and adversarial learning in generative models
Aditya Grover, Manik Dhar, and Stefano Ermon · 2018
Cited alongside, same era.
i-revnet: Deep invertible networks
Jörn-Henrik Jacobsen, Arnold W. M. Smeulders, and Edouard Oyallon · 2018
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner
Cited in the paper.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner
Cited in the paper.
Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan
Cited in the paper.
Hybrid models with deep and invertible features
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, and Balaji Lakshminarayanan
Cited in the paper.
Partha Ghosh, Arpan Losalka, and Michael J Black · 2019
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Excessive invariance causes adversarial vulnerability
Joern-Henrik Jacobsen, Jens Behrmann, Richard Zemel, and Matthias Bethge · 2019
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Towards the first adversarially robust neural network model on mnist
L. Schott, J. Rauber, W. Brendel, and M. Bethge · 2019
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