A Brief Introduction To GANs

With explanations of the math and code

GANs, or Generative Adversarial Networks, are a type of neural network architecture that allow neural networks to generate data. In the past few years, they’ve become one of the hottest subfields in deep learning, going from generating fuzzy images of digits to photorealistic images of faces.

Before: fuzzy digits, After: photorealistic faces

Variants of GANs have now done insane stuff, like converting images of zebras to horses and vice versa.

I found GANs fascinating, and in an effort to understand them better, I thought that I’d write this article, and in the process of explaining the math and code behind them, understand them better myself.

Here’s a link to a github repo I made for GAN resources: