January 2019
Intermediate to advanced
386 pages
11h 13m
English
book
Python Deep Learning - Second Edition
by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
Content preview from Python Deep Learning - Second Edition
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
Start your free trial
Generative Adversarial networks
In this section, we'll talk about arguably the most popular generative model today: the GANs framework. It was first introduced in 2014 in the landmark paper Generative Adversarial Nets(http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf) by Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair Aaron Courville, and Yoshua Bengio. The GANs framework can work with any type of data, but it's most popular application by far is to generate images, and we'll discuss them in this context only. Let's see how it work:
A GAN system
A GAN is a system of two components ...