Image Generation with GANs
Generative modeling is a powerful concept that provides us with immense potential to approximate or model underlying processes that generate data. In the previous chapters, we covered concepts associated with deep learning in general and more specifically related to restricted Boltzmann machines (RBMs) and variational autoencoders (VAEs). This chapter will introduce another family of generative models called Generative Adversarial Networks (GANs).
Heavily inspired by the concepts of game theory and picking up some of the best components from previously discussed techniques, GANs provide a powerful framework for working in the generative modeling space. Since their invention in 2014 by Goodfellow et al., GANs have benefitted from tremendous research and are now being used to explore creative domains such as art, fashion, and photography.
The following are two amazing high-quality samples from a variant of GANs called StyleGAN (Figure 6.1). The photograph...
