Generative Adversarial Networks (GANs) are deep neural net architectures that consist of two networks pitted against each other (hence the name adversarial).

GANs were introduced in a paper (https://arxiv.org/abs/1406.2661) by Ian Goodfellow and other researchers, including Yoshua Bengio, at the University of Montreal in 2014. Referring to GANs, Facebook's AI research director, Yann LeCun, called adversarial training the most interesting idea in the last 10 years in machine learning.

The potential of GANs is huge, because they can learn to mimic any distribution of data. That is, GANs can be taught to create worlds eerily similar to our own in any domain: images, music, speech, or prose. They are robot artists in a sense, and their output is impressive (https://www.nytimes.com/2017/08/14/arts/design/google-how-ai-creates-new-music-and-new-artists-project-magenta.html)—and poignant too.

The following topics will be covered in this chapter:

• An intuitive introduction
• Simple implementation of GANs
• Deep convolutional GANs