Generative Adversarial Networks

Wasserstein GAN

Introduced by Arjovsky et al. in Wasserstein GAN

Wasserstein GAN, or WGAN, is a type of generative adversarial network that minimizes an approximation of the Earth-Mover's distance (EM) rather than the Jensen-Shannon divergence as in the original GAN formulation. It leads to more stable training than original GANs with less evidence of mode collapse, as well as meaningful curves that can be used for debugging and searching hyperparameters.

Source: Wasserstein GAN


Paper Code Results Date Stars


Task Papers Share
Image Generation 14 16.09%
Test 7 8.05%
Denoising 5 5.75%
Image-to-Image Translation 4 4.60%
Time Series Analysis 4 4.60%
Translation 3 3.45%
EEG 2 2.30%
General Classification 2 2.30%
Super-Resolution 2 2.30%


Component Type