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Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives.
Integral probability metrics and their generating classes of functions
Alfred Müller · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
Earlier work this paper cites.
The mathematics of computerized tomography
Frank Natterer · 2001
Earlier work this paper cites.
Envelope theorems for arbitrary choice sets
Paul Milgrom and Ilya Segal · 2002
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Cédric Villani · 2009
Earlier work this paper cites.
Density estimation by dual ascent of the log-likelihood
Esteban G Tabak and Eric Vanden-Eijnden · 2010
Earlier work this paper cites.
The Radon transform on Rn
Sigurdur Helgason · 2011
Earlier work this paper cites.
Characterization and computation of local nash equilibria in continuous games
Lillian J Ratliff, Samuel A Burden, and S Shankar Sastry · 2013
Earlier work this paper cites.
A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
Earlier work this paper cites.
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.
Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Sliced and radon Wasserstein barycenters of measures
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Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
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Danilo Jimenez Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
f-GAN: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 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.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Generalization and equilibrium in generative adversarial nets (GANs)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
Earlier work this paper cites.
Improved training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Earlier work this paper cites.
beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Jae Hyun Lim and Jong Chul Ye · 2017
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The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
Earlier work this paper cites.
Gradient descent GAN optimization is locally stable
Vaishnavh Nagarajan and J Zico Kolter · 2017
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VEEGAN: Reducing mode collapse in gans using implicit variational learning
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
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On the limitations of first-order approximation in gan dynamics
Jerry Li, Aleksander Madry, John Peebles, and Ludwig Schmidt · 2018
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Which training methods for GANs do actually converge?
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cgans with projection discriminator
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Analyzing and improving the image quality of stylegan
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HiFi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis
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Training generative adversarial networks by solving ordinary differential equations
Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andrew Brock, Jeff Donahue, Timothy P Lillicrap, and Pushmeet Kohli · 2020
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Top-k training of GANs: Improving generators by making critics less critical
Samarth Sinha, Anirudh Goyal, Colin Raffel, and Augustus Odena · 2020
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Bridging the gap between f-gans and wasserstein gans
Jiaming Song and Stefano Ermon · 2020
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Denoising diffusion implicit models
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On the convergence and robustness of training gans with regularized optimal transport
Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, and Jason D Lee · 2018
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MoCoGAN: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
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Invertible residual networks
Jens Behrmann, Will Grathwohl, Ricky TQ Chen, David Duvenaud, and Jörn-Henrik Jacobsen · 2019
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Reducing noise in gan training with variance reduced extragradient
Tatjana Chavdarova, Gauthier Gidel, François Fleuret, and Simon Lacoste-Julien · 2019
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Max-sliced Wasserstein distance and its use for GANs
Ishan Deshpande, Yuan-Ting Hu, Ruoyu Sun, Ayis Pyrros, Nasir Siddiqui, Sanmi Koyejo, Zhizhen Zhao, David Forsyth, and Alexander G Schwing · 2019
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Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Adversarial lipschitz regularization
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Understanding and stabilizing GANs’ training dynamics with control theory
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Consistency regularization for generative adversarial networks
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GANcraft: Unsupervised 3d neural rendering of minecraft worlds
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Alias-free generative adversarial networks
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Run-sort-rerun: Escaping batch size limitations in sliced wasserstein generative models
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Why spectral normalization stabilizes GANs: Analysis and improvements
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Distributional sliced-Wasserstein and applications to generative modeling
Khai Nguyen, Nhat Ho, Tung Pham, and Hung Bui · 2021
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Improved denoising diffusion probabilistic models
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Projected gans converge faster
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Herve Jegou · 2021
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Taming gans with lookahead-minmax
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Augmented sliced wasserstein distances
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Variational wasserstein gradient flow
Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei, and Yongxin Chen · 2022
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Minimax optimization with smooth algorithmic adversaries
Tanner Fiez, Chi Jin, Praneeth Netrapalli, and Lillian J Ratliff · 2022
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Revisiting sliced wasserstein on images: From vectorization to convolution
Khai Nguyen and Nhat Ho · 2022
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Globally injective relu networks
Michael Puthawala, Konik Kothari, Matti Lassas, Ivan Dokmanić, and Maarten De Hoop · 2022
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Stylegan-xl: Scaling stylegan to large diverse datasets
Axel Sauer, Katja Schwarz, and Andreas Geiger · 2022
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SQ-VAE: Variational Bayes on discrete representation with self-annealed stochastic quantization
Yuhta Takida, Takashi Shibuya, WeiHsiang Liao, Chieh-Hsin Lai, Junki Ohmura, Toshimitsu Uesaka, Naoki Murata, Takahashi Shusuke, Toshiyuki Kumakura, and Yuki Mitsufuji · 2022
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Spherical sliced-wasserstein
Clément Bonet, Paul Berg, Nicolas Courty, François Septier, Lucas Drumetz, and Minh-Tan Pham · 2023
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Lu-net: Invertible neural networks based on matrix factorization
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The role of imagenet classes in fréchet inception distance
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Stylegan-t: Unlocking the power of gans for fast large-scale text-to-image synthesis
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