Spectral Regularization for Combating Mode Collapse in GANs

J. Heinonen, “Lectures on lipschitz analysis,” University of Jyvaskyla , 2005

2005

Earlier work this paper cites.

A. Torralba, R. Fergus, and W. T. Freeman, “80 million tiny images: A large data set for non-parametric object and scene recognition,” IEEE Transactions on Pattern Analysis and Ma-chine Intelligence , vol. 30, no. 11, pp. 901–909, 2008

2008

Earlier work this paper cites.

J. Deng, W. Dong, R. S. L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” 2009 IEEE conference on computer vision and pattern recognition , pp. 248–255, 2009

2009

Earlier work this paper cites.

I. Goodfellow, J. Pouget-Abadie, and M. Mirza, “Generative adversarial nets,” Advances in neural information processing sys-tems , pp. 2672–2680, 2014

2014

Earlier work this paper cites.

A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv , p. 1511.06434, 2015

2015

Earlier work this paper cites.

T. Salimans and D. Kingma, “Weight normalization: A simple reparameterization to accelerate training of deep neural networks,” Advances in Neural Information Processing Systems , pp. 901–909, 2016

2016

Earlier work this paper cites.

A. Brock, T. Lim, and J. M. Ritchie, “Neural photo editing with introspective adversarial networks,” arXiv preprint arXiv , p. 1609.07093, 2016

2016

Cited alongside, same era.

T. Salimans, I. Goodfellow, and W. Zaremba, “Improved techniques for training gans,” Advances in Neural Information Processing Sys-tems , pp. 2234–2242, 2016

2016

Cited alongside, same era.

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” arXiv preprint arXiv , p. 1701.07875, 2017

2017

Cited alongside, same era.

J. Wu, Z. Huang, and J. Thoma, “Energy-relaxed wassertein gans (energywgan): Towards more stable and high resolution image generation,” arXiv preprint arXiv , p. 1712.01026, 2017

2017

Cited alongside, same era.

D. Berthelot, T. Schumm, and L. Metz, “Began: Boundary equilibrium generative adversar-ial networks,” arXiv preprint arXiv , p. 1703.10717, 2017

2017

I. Gulrajani, F. Ahmed, and M. Arjovsky, “Improved training of wasserstein gans,” Advances in Neural Information Processing Systems , pp. 5769–5779, 2017

2017

Later among the works it cites.

G. Qi, “Loss-sensitive generative adversarial networks on lipschitz densities,” arXiv preprint arXiv , p. 1701.06264, 2017

2017

Later among the works it cites.

M. Heusel, H. Ramsauer, and T. Unterthiner, “Gans trained by a two time-scale update rule converge to a nash equilibrium,” arXiv preprint arXiv , p. 1706.08500, 2017

2017

Later among the works it cites.

B. Andrew, J. Donahue, and K. Simonyan, “Large scale gan training for high fidelity natural image synthesis,” arXiv preprint arXiv , 2018

2018

Later among the works it cites.

T. Miyato, T. Kataoka, and M. Koyama, “Spectral normalization for generative adversarial networks,” arXiv preprint arXiv , p. 1802.05957, 2018

2018