Properties of f-divergences and f-GAN training

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Estimating divergence functionals and the likelihood ratio by convex risk minimization

X. Nguyen, M. J. Wainwright, and M. I. Jordan · 2010

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f-GAN: Training generative neural samplers using variational divergence minimization

S. Nowozin, B. Cseke, and R. Tomioka · 2016

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Improved generator objectives for GANs

B. Poole, A. A. Alemi, J. Sohl-Dickstein, and A. Angelova · 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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Gradient descent GAN optimization is locally stable

V. Nagarajan and J. Z. Kolter · 2017

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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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Progressive growing of GANs for improved quality, stability, and variation

T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2018

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Which training methods for GANs do actually converge?

L. Mescheder, A. Geiger, and S. Nowozin · 2018

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A style-based generator architecture for generative adversarial networks

T. Karras, S. Laine, and T. Aila · 2019

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Training GANs with centripetal acceleration

Original

W. Peng, Y. Dai, H. Zhang, and L. Cheng · 2019