Hands-On Convolutional Neural Networks with TensorFlow

WGAN

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 ...