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Generative Adversarial Networks (GANs) have become a widely popular framework for generative modelling of high-dimensional datasets.
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Training generative neural networks via maximum mean discrepancy optimization
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Generative moment matching networks
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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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Towards principled methods for training generative adversarial networks
M. Arjovsky and L. Bottou · 2017
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Generalization and equilibrium in generative adversarial nets (gans)
S. Arora, R. Ge, Y. Liang, T. Ma, and Y. Zhang · 2017
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L. Theis, A. V. D. Oord, and M. Bethge · 2015
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Density estimation using real nvp
L. Dinh, J. Sohl-Dickstein, and S. Bengion · 2016
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Nips 2016 tutorial: Generative adversarial networks
I. Goodfellow · 2016
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Unrolled generative adversarial networks
L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein · 2016
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Wade-Farley, S. Ozair, and A. Courville
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Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy
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Lenet-5, convolutional neural networks
Y. LeCun et al
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Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 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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On the quantitative analysis of decoder-based generative models
Y. Wu, Y. Burda, R. Salakhutdinov, and R. Grosse · 2017
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