Primer on Generative Adversarial Networks

GAN's (Generative Adversarial Networks)  always impressed me as some of the most innovative ways to use neural networks and deep learning.   GAN's were unknown when we first used neural networks to replace statistical methods in retail behavior analysis.   Here a good primer from DSC, using image analysis examples, fairly non technical if you already do data analytics.   Worth knowing the concept, there are lots of ways out there to implement.

A Primer On Generative Adversarial Networks
Guest blog by Keshav Dhandhania and Arash Delijani in DSC

In this article, I’ll talk about Generative Adversarial Networks, or GANs for short. GANs are one of the very few machine learning techniques which has given good performance for generative tasks, or more broadly unsupervised learning. In particular, they have given splendid performance for a variety of image generation related tasks. Yann LeCun, one of the forefathers of deep learning, has called them “the best idea in machine learning in the last 10 years”. Most importantly, the core conceptual ideas associated with a GAN are quite simple to understand (and in-fact, you should have a good idea about them by the time you finish reading this article).

In this article, we’ll explain GANs by applying them to the task of generating images. The following is the outline of this article

A brief review of Deep Learning
The image generation problem
Key issue in generative tasks
Generative Adversarial Networks
Challenges
Further reading
Conclusion .... "