Introduction: Image Generation with GANs

Generative models are a class of models in the unsupervised machine learning space. They help us model the underlying distributions responsible for generating the dataset under consideration. Different methods/frameworks work with generative models. Generative modeling is a powerful concept that provides us with immense potential to approximate or model underlying processes that generate data.

This chapter will introduce another family of generative models called generative adversarial networks (GANs). We’ll explore the following topics:

• The taxonomy of generative models.

• A number of improved GANs, such as DCGAN and Conditional-GAN.

• The progressive GAN setup and its various components.

• Some of the challenges associated with GANs.

• Hands-on examples.