Make Your Own Algorithmic Art

This is the Part 6 of a short series of posts introducing and building generative adversarial networks, known as GANs. In this post we will develop a system for testing a GAN using controllable synthetic data. Too often GANs are tested against datasets which are very varied and this makes assessing the GAN very difficult. We'll also do some experiments with some of the many GAN design options to see if they help or hinder. Using controlled and simpler synthetic image data makes this assessment easier. Output from a conditioned GAN learning four classes of synthetic image. Previously: Part 1 introduced the idea of adversarial learning and we started to build the machinery of a GAN implementation. Part 2 we extended our code to learn a simple 1-dimensional pattern 1010. Part 3 we developed our code to learn to generate 2-dimensional grey-scale images that look like handwritten digits Part 4 extended our code to learn full colour faces, and also developed convolutional networks to encourage learning localised image features Part 5 developed a conditional GAN that can be trained to output images of a desired class.