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