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Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks.
Supervised learning from incomplete data via an em approach
Zoubin Ghahramani and Michael I Jordan · 1994
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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 the uniqueness of nonnegative sparse solutions to underdetermined systems of equations
Alfred M Bruckstein, Michael Elad, and Michael Zibulevsky · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Sum-Product Networks: a new deep architecture
Hoifung Poon and Pedro Domingos · 2011
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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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Statistical analysis with missing data , volume 333
Roderick JA Little and Donald B Rubin · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Cited alongside, same era.
Learning in implicit generative models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei Efros · 2016
Cited alongside, same era.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
Began: Boundary equilibrium generative adversarial networks
David Berthelot, Tom Schumm, and Luke Metz · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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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
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Semantic image inpainting with deep generative models
Raymond A Yeh, Chen Chen, Teck Yian Lim, Alexander G Schwing, Mark Hasegawa-Johnson, and Minh N Do · 2017
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AmbientGAN: Generative models from lossy measurements
Ashish Bora, Eric Price, and Alexandros G Dimakis · 2018
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Or Sharir, Ronen Tamari, Nadav Cohen, and Amnon Shashua · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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GAIN: Missing data imputation using generative adversarial nets
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2018
Later among the works it cites.