Best Resources for Getting Started With GANs
Last Updated on July 12, 2019
Generative Adversarial Networks, or GANs, are a type of deep learning technique for generative modeling.
GANs are the techniques behind the startlingly photorealistic generation of human faces, as well as impressive image translation tasks such as photo colorization, face de-aging, super-resolution, and more.
It can be very challenging to get started with GANs. This is both because the field is very young, starting with the first paper in 2014, and because of the vast number of papers and applications published every month on the topic.
In this post, you will discover the best resources that you can use to learn about generative adversarial networks.
After reading this post, you will know:
- What a generative adversarial network is and examples of specific applications for the technique.
- Video tutorials and lectures on GANs presented by the inventor of the technique.
- Reading list including the most read papers on GANs and books on deep generative models.
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.
This tutorial is divided into five parts; they are: