A Tour of Generative Adversarial Network Models

Last Updated on July 12, 2019

Generative Adversarial Networks, or GANs, are deep learning architecture generative models that have seen wide success.

There are thousands of papers on GANs and many hundreds of named-GANs, that is, models with a defined name that often includes “GAN“, such as DCGAN, as opposed to a minor extension to the method. Given the vast size of the GAN literature and number of models, it can be, at the very least, confusing and frustrating as to know what GAN models to focus on.

In this post, you will discover the Generative Adversarial Network models that you need to know to establish a useful and productive foundation in the field.

After reading this post, you will know:

• The foundation GAN models that provide the basis for the field of study.
• The extension GAN models that build upon what works and lead the way for more advanced models.
• The advanced GAN models that push the limits of the architecture and achieve impressive results.

Kick-start your project with my new book Generative Adversarial Networks with Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.