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Wasserstein-GANs have been introduced to address the deficiencies of generative adversarial networks (GANs) regarding the problems of vanishing gradients and mode collapse during the training, leading to improved convergence behaviour and improved image quality.
An iterative algorithm for computing the best estimate of an orthogonal matrix
Åke Björck and Clazett Bowie · 1971
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Optimal transport: old and new
Cédric Villani · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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A feasible method for optimization with orthogonality constraints
Zaiwen Wen and Wotao Yin · 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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Exact solutions to the nonlinear dynamics of learning in deep linear neural network
Andrew M. Saxe, James L. Mcclelland, and Surya Ganguli · 2014
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Natural neural networks
Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, and Koray Kavukcuoglu · 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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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
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Generalized backpropagation, étude de cas: Orthogonality
Mehrtash Harandi and Basura Fernando · 2016
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Generating images with recurrent adversarial networks
Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang, and Roland Memisevic · 2016
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Optimization on submanifolds of convolution kernels in cnns
Mete Ozay and Takayuki Okatani · 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.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
Cited alongside, same era.
Full-capacity unitary recurrent neural networks
Scott Wisdom, Thomas Powers, John Hershey, Jonathan Le Roux, and Les Atlas · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Spectral norm regularization for improving the generalizability of deep learning
Yuichi Yoshida and Takeru Miyato · 2017
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Banach wasserstein gan
Jonas Adler and Sebastian Lunz · 2018
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Sorting out lipschitz function approximation
Cem Anil, James Lucas, and Roger Grosse · 2018
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Can we gain more from orthogonality regularizations in training deep cnns?
Nitin Bansal, Xiaohan Chen, and Zhangyang Wang · 2018
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Orthogonal weight normalization: Solution to optimization over multiple dependent stiefel manifolds in deep neural networks
Lei Huang, Xianglong Liu, Bo Lang, Adams Yu, Yongliang Wang, and Bo Li · 2018
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Sanjeev Arora and Yi Zhang · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 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.
Improving training of deep neural networks via singular value bounding
Kui Jia, Dacheng Tao, Shenghua Gao, and Xiangmin Xu · 2017
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Cited alongside, same era.
Regularizing cnns with locally constrained decorrelations
Pau Rodríguez, Jordi Gonzàlez, Guillem Cucurull, Josep M. Gonfaus, and F. Xavier Roca · 2017
Cited alongside, same era.
Veegan: Reducing mode collapse in gans using implicit variational learning
Akash Srivastava, Lazar Valkoz, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
Cited alongside, same era.
Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 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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On the regularization of wasserstein GANs
Henning Petzka, Asja Fischer, and Denis Lukovnikov · 2018
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On gans and gmms
Eitan Richardson and Yair Weiss · 2018
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A classification-based study of covariate shift in gan distributions
Shibani Santurkar, Ludwig Schmidt, and Aleksander Madry · 2018
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How does batch normalization help optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Closest in time.
https://github.com/pfnet-research/sngan_projection/issues/15#issuecomment-392680419
Takeru Miyato · 2019
Closest in time.