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Generative adversarial networks (GANs) are powerful tools for learning generative models.
Iterative solution of games by fictitious play
Brown, G.W.: · 1951
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Fictitious play for continuous games
Danskin, J.M.: · 1954
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On general minimax theorems
Sion, M.: · 1958
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X., Bengio, Y.: · 2010
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.L.: · 2014
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Generative adversarial text to image synthesis
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: · 2016
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Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., Fei-Fei, L.: · 2016
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Deep structured energy based models for anomaly detection
Zhai, S., Cheng, Y., Lu, W., Zhang, Z.: · 2016
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f-GAN: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., Tomioka, R.: · 2016
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Generative multi-adversarial networks
Durugkar, I., Gemp, I., Mahadevan, S.: · 2016
Cited alongside, same era.
Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: · 2016
Cited alongside, same era.
Nips 2016 tutorial: Generative adversarial networks
Goodfellow, I.: · 2016
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., Chintala, S.: · 2016
Cited alongside, same era.
Learning from simulated and unsupervised images through adversarial training
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: · 2017
Cited alongside, same era.
Unrolled generative adversarial networks
Metz, L., Poole, B., Pfau, D., Sohl-Dickstein, J.: · 2017
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Generalization and equilibrium in generative adversarial nets (gans)
Arora, S., Ge, R., Liang, Y., Ma, T., Zhang, Y.: · 2017
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Multi-generator gernerative adversarial nets
Hoang, Q., Nguyen, T.D., Le, T., Phung, D.: · 2017
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Multi-agent diverse generative adversarial networks
Ghosh, A., Kulharia, V., Namboodiri, V., Torr, P.H., Dokania, P.K.: · 2017
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Dual discriminator generative adversarial nets
Nguyen, T., Le, T., Vu, H., Phung, D.: · 2017
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Adagan: Boosting generative models
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Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: · 2017
Cited alongside, same era.
Gradient descent gan optimization is locally stable
Nagarajan, V., Kolter, J.Z.: · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: · 2017
Cited alongside, same era.
Towards understanding the dynamics of generative adversarial networks
Li, J., Madry, A., Peebles, J., Schmidt, L.: · 2017
Cited alongside, same era.
Mode regularized generative adversarial networks
Che, T., Li, Y., Jacob, A.P., Bengio, Y., Li, W.: · 2017
Cited alongside, same era.
Tolstikhin, I.O., Gelly, S., Bousquet, O., Simon-Gabriel, C.J., Schölkopf, B.: · 2017
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Stacked generative adversarial networks
Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., Belongie, S.: · 2017
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Training generative adversarial networks via primal-dual subgradient methods: A lagrangian perspective on gan
Chen, X., Wang, J., Ge, H.: · 2018
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Beyond local nash equilibria for adversarial networks
Oliehoek, F.A., Savani, R., Gallego, J., van der Pol, E., Gross, R.: · 2018
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An online learning approach to generative adversarial networks
Grnarova, P., Levy, K.Y., Lucchi, A., Hofmann, T., Krause, A.: · 2018
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