Generative Models
Generative models are a type of machine learning algorithm that is used to create data. They are used to generate new data that is similar to the data that was used to train the model. They can be used to create new data for testing or to fill in missing data. Generative models are used in many applications, such as density estimation, image synthesis, and natural language processing. The VAE discussed in Chapter 8, Autoencoders, was one type of generative model; in this chapter, we will discuss a wide range of generative models, Generative Adversarial Networks (GANs) and their variants, flow-based models, and diffusion models.
GANs have been defined as the most interesting idea in the last 10 years in ML (https://www.quora.com/What-are-some-recent-and-potentially-upcoming-breakthroughs-in-deep-learning) by Yann LeCun, one of the fathers of deep learning. GANs are able to learn how to reproduce synthetic data that looks real. For instance, computers can learn...
