How to Implement Wasserstein Loss for Generative Adversarial Networks
The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the stability when training the model and provides a los...
Source: machinelearningmastery.com
The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the stability when training the model and provides a loss function that correlates with the quality of generated images. It is an important extension to the GAN model and requires a conceptual shift away from a […]