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We study the problem of learning conditional generators from noisy labeled samples, where the labels are corrupted by random noise.
Maximum likelihood estimation of observer error-rates using the em algorithm
Alexander Philip Dawid and Allan M Skene · 1979
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The mnist database of handwritten digits
Yann LeCun · 1998
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Building text classifiers using positive and unlabeled examples
Bing Liu, Yang Dai, Xiaoli Li, Wee Sun Lee, and Philip S Yu · 2003
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Learning kernel perceptrons on noisy data using random projections
Guillaume Stempfel and Liva Ralaivola · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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On integral probability metrics, ϕ \phi -divergences and binary classification
Bharath K Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, and Gert RG Lanckriet · 2009
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Learning svms from sloppily labeled data
Guillaume Stempfel and Liva Ralaivola · 2009
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Support vector machines under adversarial label noise
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2011
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Iterative learning for reliable crowdsourcing systems
David R Karger, Sewoong Oh, and Devavrat Shah · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 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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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Deep generative image models using a Laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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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, et al · 2015
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2016
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Plug & play generative networks: Conditional iterative generation of images in latent space
Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, and Jeff Clune · 2016
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Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2016
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Generative adversarial text to image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 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 Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alex Smola, and Arthur Gretton · 2016
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Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Dual motion gan for future-flow embedded video prediction
Xiaodan Liang, Lisa Lee, Wei Dai, and Eric P Xing · 2017
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PacGAN: The power of two samples in generative adversarial networks
Zinan Lin, Ashish Khetan, Giulia Fanti, and Sewoong Oh · 2017
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Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russ Webb · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaolei Huang, Xiaogang Wang, and Dimitris Metaxas · 2017
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On the discrimination-generalization tradeoff in GANs
Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, and Xiaodong He · 2017
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum · 2016
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Semantic image inpainting with perceptual and contextual losses
Raymond Yeh, Chen Chen, Teck Yian Lim, Mark Hasegawa-Johnson, and Minh N Do · 2016
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Generative visual manipulation on the natural image manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A Efros · 2016
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Representation learning and adversarial generation of 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2017
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
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Began: Boundary equilibrium generative adversarial networks
David Berthelot, Tom Schumm, and Luke Metz · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Approximability of discriminators implies diversity in GANs
Yu Bai, Tengyu Ma, and Andrej Risteski · 2018
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Some theoretical properties of GANs
G Biau, B Cadre, M Sangnier, and U Tanielian · 2018
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Mikołaj Bińkowski, Dougal J Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Ambientgan: Generative models from lossy measurements
Ashish Bora, Eric Price, and Alexandros G Dimakis · 2018
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The relativistic discriminator: a key element missing from standard GAN
Alexia Jolicoeur-Martineau · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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cGANs with projection discriminator
Takeru Miyato and Masanori Koyama · 2018
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Clustergan: Latent space clustering in generative adversarial networks
Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, and Sreeram Kannan · 2018
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Solving approximate Wasserstein GANs to stationarity
Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, and Jason D Lee · 2018
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Mimic and classify: A meta-algorithm for conditional independence testing
Rajat Sen, Karthikeyan Shanmugam, Himanshu Asnani, Arman Rahimzamani, and Sreeram Kannan · 2018
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Compressed sensing with deep image prior and learned regularization
David Van Veen, Ajil Jalal, Eric Price, Sriram Vishwanath, and Alexandros G Dimakis · 2018
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Robust gans against dishonest adversaries
Zhi Xu, Chengtao Li, and Stefanie Jegelka · 2018
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Self-attention generative adversarial networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena · 2018
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