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Detecting overfitting in generative models is an important challenge in machine learning.
On a test of whether one of two random variables is stochastically larger than the other
Henry B Mann and Donald R Whitney · 1947
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Towards GAN benchmarks which require generalization
Ishaan Gulrajani, Colin Raffel, and Luke Metz · 2001
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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A test of relative similarity for model selection in generative models
Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko, Ioannis Antonoglou, and Arthur Gretton · 2016
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Mode regularized generative adversarial networks
Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li · 2016
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Revisiting classifier two-sample tests
David Lopez-Paz and Maxime Oquab · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Generative models and model criticism via optimized maximum mean discrepancy
Dougal J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, and Arthur Gretton · 2016
Cited alongside, same era.
A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2016
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Real-valued (medical) time series generation with recurrent conditional gans
Large scale gan training for high fidelity natural image synthesis, 2018
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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On gans and gmms
Eitan Richardson and Yair Weiss · 2018
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Assessing generative models via precision and recall
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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An empirical study on evaluation metrics of generative adversarial networks
Qiantong Xu, Gao Huang, Yang Yuan, Chuan Guo, Yu Sun, Felix Wu, and Kilian Weinberger · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Improved precision and recall metric for assessing generative models
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Cristóbal Esteban, Stephanie L Hyland, and Gunnar Rätsch · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
On the quantitative analysis of decoder-based generative models
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger B. Grosse · 2017
Cited alongside, same era.
Can vaes generate novel examples?
Alican Bozkurt, Babak Esmaeili, Dana H Brooks, Jennifer G Dy, and Jan-Willem van de Meent · 2018
Cited alongside, same era.
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
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Detecting overfitting of deep generative networks via latent recovery
Ryan Webster, Julien Rabin, Loïc Simon, and Frédéric Jurie · 2019
Later among the works it cites.