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Generative adversarial networks (GANs) evolved into one of the most successful unsupervised techniques for generating realistic images.
A stochastic approximation method
H. Robbins and S. Monro · 1951
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M. Schwartz · 1972
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General potential surfaces and neural networks
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Coulomb potential learning
M. P. Perrone and L. N. Cooper · 1995
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GTM: the generative topographic mapping
C. Bishop, M. Svensén, and C. Williams · 1998
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Efficient BackProp
Y. LeCun, L. Bottou, G. Orr, and K. R. Müller · 1998
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Coulomb classifiers: Reinterpreting SVMs as electrostatic systems
S. Hochreiter and M. C. Mozer · 2001
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Coulomb classifiers: Generalizing support vector machines via an analogy to electrostatic systems
S. Hochreiter, M. C. Mozer, and K. Obermayer · 2003
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Optimal kernels for unsupervised learning
S. Hochreiter and K. Obermayer · 2005
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A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
M. U. Gutmann and A. Hyvärinen · 2012
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One billion word benchmark for measuring progress in statistical language modeling
C. Chelba, T. Mikolov, M. Schuster, Q. Ge, T. Brants, P. Koehn, and T. Robinson · 2013
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Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
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Spherical Harmonics in p p Dimensions
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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On distinguishability criteria for estimating generative models
I. J. Goodfellow · 2014
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The loss surfaces of multilayer networks
A. Choromanska, M. Henaff, M. Mathieu, G. B. Arous, and Y. LeCun · 2015
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Training generative neural networks via maximum mean discrepancy optimization
G. K. Dziugaite, D. M. Roy, and Z. Ghahramani · 2015
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Generative moment matching networks
Y. Li, K. Swersky, and R. Zemel · 2015
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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Towards principled methods for training generative adversarial networks
M. Arjovsky and L. Bottou · 2017
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Do GANs actually learn the distribution? An empirical study
S. Arora and Y. Zhang · 2017
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BEGAN: boundary equilibrium generative adversarial networks
D. Berthelot, T. Schumm, and L. Metz · 2017
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Mode regularized generative adversarial networks
T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li · 2017
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NIPS 2016 tutorial: Generative adversarial networks
I. J. Goodfellow · 2017
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LSUN: construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao · 2015
Cited alongside, same era.
How to train a GAN? Tips and tricks to make GANs work
S. Chintala, E. Denton, M. Arjovsky, and M. Mathieu · 2016
Cited alongside, same era.
Fast and accurate deep network learning by exponential linear units (ELUs)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2016
Cited alongside, same era.
Deep learning without poor local minima
K. Kawaguchi · 2016
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2016
Cited alongside, same era.
Unrolled generative adversarial networks
L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein · 2016
Cited alongside, same era.
Improved training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2017
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GANs trained by a two time-scale update rule converge to a Nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, G. Klambauer, and S. Hochreiter · 2017
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Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
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Self-normalizing neural networks
G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter · 2017
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MMD GAN: Towards Deeper Understanding of Moment Matching Network
C-L. Li, W-C. Chang, Y. Cheng, Y. Yang, and B. Póczos · 2017
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Geometric GAN
J. H. Lim and J. C. Ye · 2017
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McGan: Mean and covariance feature matching GAN
Y. Mroueh, T. Sercu, and V. Goel · 2017
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Energy-based generative adversarial network
J. J. Zhao, M. Mathieu, and Y. LeCun · 2017
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