Learning What and Where to Draw

Introduction

Generative Adversarial Networks (GANs) have been successfully used to synthesize compelling real-world images. In what follows we outline an enhanced GAN called the Generative Adversarial What- Where Network (GAWWN). In addition to accepting as input a noise vector, this network also accepts as input instructions describing what content to draw and in which location to draw the content. Traditionally, these models use simply conditioning variables such as a class label or a non-localized caption. The authors of 'Learning What and Where to Draw'