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Wasserstein Divergence for GANs

Jiqing Wu, Zhiwu Huang, Janine Thoma, Dinesh Acharya, Luc Van Gool.
European Conference on Computer Vision (ECCV)
September 2018

Abstract

In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the family of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and competitive qualitative performance. However, it is very challenging to approximate the k-Lipschitz constraint required by the Wasserstein-1 metric (W-met). In this paper, we propose a novel Wasserstein divergence (W-div), which is a relaxed version of W-met and does not require the k-Lipschitz constraint. As a concrete application, we introduce a Wasserstein divergence objective for GANs (WGAN-div), which can faithfully approximate Wdiv through optimization. Under various settings, including progressive growing training, we demonstrate the stability of the proposed WGANdiv owing to its theoretical and practical advantages over WGANs. Also, we study the quantitative and visual performance of WGAN-div on standard image synthesis benchmarks, showing the superior performance of WGAN-div compared to the state-of-the-art methods.


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@InProceedings{eth_biwi_01440,
  author = {Jiqing Wu and Zhiwu Huang and Janine Thoma and Dinesh Acharya and Luc Van Gool. },
  title = {Wasserstein Divergence for GANs },
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2018},
  month = {September},
  keywords = {}
}