Image Generator - Drawing Cartoons with Generative Adversarial Networks

Generating Simpsons with DCGANs

In today’s article, we are going to implement a machine learning model that can generate an infinite number of alike image samples based on a given dataset. In order to do so, we are going to demystify Generative Adversarial Networks (GANs) and feed it with a dataset containing characters from ‘The Simspons’. By the end of this article, you will be familiar with the basics behind the GANs and you will be able to build a generative model on your own!

To get a better idea about the GANs’ capabilities, take a look at the following example of the Homer Simpson evolution during the training process.

Fascinating, right?

Let’s dive into some theory to get a better understanding of how it actually works.

Generative Adversarial Networks (GANs)

Let’s start our GAN journey with defining a problem that we are going to solve.

We would like to provide a set of images as an input, and generate samples based on them as an output.

Input Images -> GAN -> Output Samples

With the following problem definition, GANs fall into the Unsupervised Learning bucket because…