Generative Adversarial Networks
In this chapter we will discuss Generative Adversarial Networks (GANs) and its variants. 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 how to paint and create realistic images. The idea was originally proposed by Ian Goodfellow (for more information refer to NIPS 2016 Tutorial: Generative Adversarial Networks, by I. Goodfellow, 2016); he has worked with the University of Montreal, Google Brain, and OpenAI, and is presently working in Apple Inc as the Director of Machine Learning.
In this chapter we will cover different types of GANs and see some of their implementation in TensorFlow 2.0. Broadly we will cover the following topics:
- What is a GAN?
- Deep...
