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Generative adversarial networks (GANs) are innovative techniques for learning generative models of complex data distributions from samples.
Equivalent comparisons of experiments
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The mnist database of handwritten digits
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A proof of the fisher information inequality via a data processing argument
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A practical guide to training restricted boltzmann machines
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Hilbert space embeddings and metrics on probability measures
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 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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Extremal mechanisms for local differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2014
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Adam: A method for stochastic optimization
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Xsede: accelerating scientific discovery
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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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Secure multi-party differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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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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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Bridges: a uniquely flexible hpc resource for new communities and data analytics
Nicholas A Nystrom, Michael J Levine, Ralph Z Roskies, and J Scott · 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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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
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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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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, and Aaron Courville · 2016
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2017
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabas Poczos · 2017
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Towards understanding the dynamics of generative adversarial networks
Jerry Li, Aleksander Madry, John Peebles, and Ludwig Schmidt · 2017
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Dualing gans
Yujia Li, Alexander Schwing, Kuan-Chieh Wang, and Richard Zemel · 2017
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Approximation and convergence properties of generative adversarial learning
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Chelsea Finn, Paul Christiano, Pieter Abbeel, and Sergey Levine · 2016
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Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 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, and Zehan Wang · 2016
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Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 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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Shuang Liu, Olivier Bousquet, and Kamalika Chaudhuri · 2017
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The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Phase space sampling and operator confidence with generative adversarial networks
Kyle Mills and Isaac Tamblyn · 2017
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Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J. Zico Kolter · 2017
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Dual discriminator generative adversarial nets
Tu Nguyen, Trung Le, Hung Vu, and Dinh Phung · 2017
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f-gans in an information geometric nutshell
Richard Nock, Zac Cranko, Aditya K Menon, Lizhen Qu, and Robert C Williamson · 2017
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Stabilizing training of generative adversarial networks through regularization
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann · 2017
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Bayesian gans
Yunus Saatci and Andrew Wilson · 2017
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A classification-based perspective on GAN distributions
Shibani Santurkar, Ludwig Schmidt, and Aleksander Madry · 2017
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Veegan: Reducing mode collapse in gans using implicit variational learning
Akash Srivastava, Lazar Valkov, Chris Russell, Michael Gutmann, and Charles Sutton · 2017
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Adagan: Boosting generative models
Ilya Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, and Bernhard Schölkopf · 2017
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Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 2017
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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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Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
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The inductive bias of restricted f-gans
Shuang Liu and Kamalika Chaudhuri · 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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Eitan Richardson and Yair Weiss · 2018
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Attention-based graph neural network for semi-supervised learning
Kiran K Thekumparampil, Chong Wang, Sewoong Oh, and Li-Jia Li · 2018
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