Wasserstein GAN is another variant of GANs that solve an issue that can happen when training GANs, called mode collapse. Moreover, it aims to give a metric that indicates when the GAN has converged, in other words, a loss function where the value has a meaning.

Important changes are to remove log from loss and clip the discriminator weights.

Also, follow these steps:

• Train discriminator more than generator
• Clip the weights of discriminator
• Use RMSProp instead of Adam
• Use low learning rates (0.0005)

A disadvantage of WGANs is that they are slower to train :

The image results produced by WGAN are still not that great, but this model does manage to help solve the mode collapse issue.