wGAN-GP

CellStrat > Research/Blog > wGAN-GP
19
Dec
Generative Modelling with wGAN (Wasserstein GAN)

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Last Saturday, our AI Lab team Lead Abdul Azeez presented an extensive hands-on workshop on wGANs or Wasserstein GANs. Overview of wGANs :- A GAN is a pair of neural networks – Generator and Discriminator. The G tries to produce real-like images and D tries to distinguish the generated images from real […]

Posted in: Computer Vision, Deep Learning, Generative Modeling,
Tags: 1-Lipschitz function, Earth-Mover Distance, EM Distance, GAN, generative adversarial network, generative modelling, gradient penalty, Jensen-Shannon divergence, JS Divergence, KL Divergence, Kullback-Leibler divergence, Wasserstein distance, Wasserstein GAN, wGAN, wGAN-GP, wGANs,