CAN (Creative Adversarial Network) - Explained

Lately, GANs (Generative Adversarial Networks) have been really successful in creating interesting content that are fairly abstract and hard to create procedurally. This paper, aptly named CAN (Creative, instead of Generative, Adversarial Networks) explores the possibility of machine generated creative content. This article assumes familiarity with neural networks, and essential aspects of them, including Loss Functions and Convolutions. I will follow the paper's structure as much as I can. I will add my own bits to help better understand the material.