6. 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 model called Generative Advaersarial Networks(GANs).

Heavily inspired by the concepts of game theory and picking up some of the best components from preiously discussed tetchniques, 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 termendous research and are now being used to explore creative domains such as art, fashion, and photography.

The following are two amazing high0quality samples from a variant of GANs called StyleGAN(Figure 6.1). The photograph of the kid is actually a fictional person who does not exist. The art sample is also generated by a similar network. StyleGANs are able to generrate high-quality sharp images by using the concept oof progressive growth (we will cover this in detail in later sections). These outputs were generated using the StyleGAN2 model trained on datasets such as the Flickr-Face-HQ or FFHQ dataset.

This chapter will cover:

- The taxonomy of generative models

- A number of improved GANs, such as DCGAN, Conditional-GAN, and so on

- The progressive GAN setup and its various components

- Some of the challenges associated with GANs

- Hands-on examples