Improving Generalization and Stability of Generative Adversarial Networks

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Unsupervised representation learning with deep convolutional generative adversarial networks

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Alec Radford, Luke Metz, and Soumith Chintala · 2015

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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017

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Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017

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Improved training of wasserstein gans

Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017

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Gans trained by a two time-scale update rule converge to a local nash equilibrium

Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017

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Fisher gan

Youssef Mroueh and Tom Sercu · 2017

Cited alongside, same era.

Gradient descent gan optimization is locally stable

Vaishnavh Nagarajan and J. Zico Kolter · 2017

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Automatic differentiation in pytorch

Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017

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Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities

G.-J. Qi · 2017

Cited alongside, same era.

Many paths to equilibrium: GANs do not need to decrease a divergence at every step

William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, and Ian Goodfellow · 2018

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

Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018

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Mixed batches and symmetric discriminators for GAN training

Thomas Lucas, Corentin Tallec, Yann Ollivier, and Jakob Verbeek · 2018

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Which training methods for GANs do actually converge?

Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018

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Spectral normalization for generative adversarial networks

Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018

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Sobolev GAN

Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, and Yu Cheng · 2018