On the regularization of Wasserstein GANs

@article{Petzka2017OnTR,
  title={On the regularization of Wasserstein GANs},
  author={Henning Petzka and Asja Fischer and Denis Lukovnicov},
  journal={CoRR},
  year={2017},
  volume={abs/1709.08894}
}
Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unlabeled) data. Convergence problems during training are overcome by Wasserstein GANs which minimize the distance between the model and the empirical distribution in terms of a different metric, but thereby introduce a Lipschitz constraint into the optimization problem. A simple way to enforce the Lipschitz constraint on the class of functions, which can be… CONTINUE READING
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Showing 1-10 of 21 references

Optimal Transport: Old and New

Cédric Villani
Grundlehren der mathematischen Wissenschaften. Springer Berlin Heidelberg, • 2008
View 5 Excerpts
Highly Influenced

Generative Adversarial Nets

View 4 Excerpts
Highly Influenced

Improved Techniques for Training GANs

NIPS • 2016
View 6 Excerpts
Highly Influenced

2017) point out, it does not matter whether to maximize over 1-Lipschitz or αLipschitz continuous functions, since we can equivalently optimize α ·W (μ, ν) instead of W (μ, ν)

Arjovsky
2017

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