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Generative adversarial networks have been very successful in generative modeling, however they remain relatively challenging to train compared to standard deep neural networks.
The extragradient method for finding saddle points and other problems
G. Korpelevich · 1976
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W. Paul · 1990
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Fast exact multiplication by the hessian
B. A. Pearlmutter · 1994
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The rotating-saddle trap: A mechanical analogy to rf-electric-quadrupole ion trapping?
R. Thompson, T. Harmon, and M. Ball · 2002
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Understanding the difficulty of training deep feedforward neural networks
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MNIST handwritten digit database
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Y. N. Dauphin, R. Pascanu, C. Gulcehre, K. Cho, S. Ganguli, and Y. Bengio · 2014
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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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The loss surfaces of multilayer networks
A. Choromanska, M. Henaff, M. Mathieu, G. B. Arous, and Y. LeCun · 2015
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Qualitatively characterizing neural network optimization problems
I. J. Goodfellow, O. Vinyals, and A. M. Saxe · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Neurips 2016 tutorial: Generative adversarial networks
I. Goodfellow · 2016
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Deep learning without poor local minima
K. Kawaguchi · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
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On the characterization of local nash equilibria in continuous games
L. J. Ratliff, S. A. Burden, and S. S. Sastry · 2016
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Eigenvalues of the hessian in deep learning: Singularity and beyond
L. Sagun, L. Bottou, and Y. LeCun · 2016
Empirical analysis of the hessian of over-parametrized neural networks
L. Sagun, U. Evci, V. U. Guney, Y. Dauphin, and L. Bottou · 2017
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Local saddle point optimization: A curvature exploitation approach
L. Adolphs, H. Daneshmand, A. Lucchi, and T. Hofmann · 2018
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The mechanics of n-player differentiable games
D. Balduzzi, S. Racaniere, J. Martens, J. Foerster, K. Tuyls, and T. Graepel · 2018
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Training GANs with optimism
C. Daskalakis, A. Ilyas, V. Syrgkanis, and H. Zeng · 2018
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Essentially no barriers in neural network energy landscape
F. Draxler, K. Veschgini, M. Salmhofer, and F. Hamprecht · 2018
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On the convergence of gradient-based learning in continuous games
E. Mazumdar and L. J. Ratliff · 2018
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Improved techniques for training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Improved training of wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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GANs trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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The numerics of GANs
L. Mescheder, S. Nowozin, and A. Geiger · 2017
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Gradient descent GAN optimization is locally stable
V. Nagarajan and J. Z. Kolter · 2017
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Which Training Methods for GANs do actually Converge?
L. Mescheder, A. Geiger, and S. Nowozin · 2018
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Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
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Negative eigenvalues of the hessian in deep neural networks
G. Alain, N. Le Roux, and P.-A. Manzagol · 2019
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Large scale GAN training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2019
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On finding local nash equilibria (and only local nash equilibria) in zero-sum games
E. V. Mazumdar, M. I. Jordan, and S. S. Sastry · 2019
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The unusual effectiveness of averaging in GAN training
Y. Yazıcı, C.-S. Foo, S. Winkler, K.-H. Yap, G. Piliouras, and V. Chandrasekhar · 2019
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