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Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions.
Eine informationstheoretische ungleichung und ihre anwendung auf beweis der ergodizitaet von markoffschen ketten
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
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Generative adversarial imitation learning
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Mohamed, S. and Lakshminarayanan, B · 2016
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f-GAN: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Generative adversarial nets from a density ratio estimation perspective
Uehara, M., Sato, I., Suzuki, M., Nakayama, K., and Matsuo, Y · 2016
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Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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A convex duality framework for GANs
Farnia, F. and Tse, D · 2018
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Learning deep representations by mutual information estimation and maximization
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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
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Spectral normalization for generative adversarial networks
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Learning implicit generative models with the method of learned moments
Ravuri, S., Mohamed, S., Rosca, M., and Vinyals, O · 2018
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Chi-square generative adversarial network
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Metropolis-Hastings generative adversarial networks
Turner, R., Hung, J., Frank, E., Saatci, Y., and Yosinski, J · 2018
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Learning deep kernels for exponential family densities
Wenliang, L., Sutherland, D., Strathmann, H., and Gretton, A · 2018
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Bias correction of learned generative models using Likelihood-Free importance weighting
Grover, A., Song, J., Agarwal, A., Tran, K., Kapoor, A., Horvitz, E., and Ermon, S · 2019
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