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Generative adversarial networks (GANs) are notoriously difficult to train and the reasons underlying their (non-)convergence behaviors are still not completely understood.
80 million tiny images: A large data set for nonparametric object and scene recognition
Torralba, A., Fergus, R., and Freeman, W. T. (2008) · 1970
Earlier work this paper cites.
Dynamical systems: differential equations, maps, and chaotic behaviour
Arrowsmith, D. and Place, C. M. (1992) · 1992
Earlier work this paper cites.
Lecture 6.5—rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T. and Hinton, G. (2012) · 2012
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S. (2015) · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · 2015
Earlier work this paper cites.
Nips 2016 tutorial: Generative adversarial networks
Goodfellow, I. (2016) · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Generative adversarial imitation learning
Ho, J. and Ermon, S. (2016) · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R. (2016) · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016) · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L. (2017) · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C. (2017) · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017) · 2017
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The limit points of (optimistic) gradient descent in min-max optimization
Daskalakis, C. and Panageas, I. (2018) · 2018
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On the limitations of first-order approximation in gan dynamics
Li, J., Madry, A., Peebles, J., and Schmidt, L. (2018) · 2018
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Which training methods for gans do actually converge?
Mescheder, L., Geiger, A., and Nowozin, S. (2018) · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (2018) · 2018
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Stabilizing adversarial nets with prediction methods
Yadav, A., Shah, S., Xu, Z., Jacobs, D., and Goldstein, T. (2018) · 2018
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K. (2019) · 2019
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The numerics of gans
Mescheder, L., Nowozin, S., and Geiger, A. (2017) · 2017
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Gradient descent gan optimization is locally stable
Nagarajan, V. and Kolter, J. Z. (2017) · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A. (2017) · 2017
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The mechanics of n-player differentiable games
Balduzzi, D., Racaniere, S., Martens, J., Foerster, J., Tuyls, K., and Graepel, T. (2018) · 2018
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Training gans with optimism
Daskalakis, C., Ilyas, A., Syrgkanis, V., and Zeng, H. (2018) · 2018
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A variational inequality perspective on generative adversarial nets
Gidel, G., Berard, H., Vincent, P., and Lacoste-Julien, S. (2019) · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T. (2019) · 2019
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Stable opponent shaping in differentiable games
Letcher, A., Foerster, J., Balduzzi, D., Rocktäschel, T., and Whiteson, S. (2019) · 2019
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Interaction matters: A note on non-asymptotic local convergence of generative adversarial networks
Liang, T. and Stokes, J. (2019) · 2019
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Stabilizing training of generative adversarial networks through regularization
Roth, K., Lucchi, A., Nowozin, S., and Hofmann, T. (2017) · 2025
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