Generative Adversarial Networks
Tutorials
Intuition Behind GANs
In this tutorial, we explore the basic structure of generative adversarial networks to gain some intuition about how it works.
Discriminator
Now we know generative adversarial networks consist of 2 networks, generator and discriminator. In this tutorial, we will see how the discriminator works and its code.
Generator
We have seen how the discriminator works, in this tutorial, we will look at how the generator works.
Training GAN
In the previous tutorials, we have seen the 2 components in a generative adversarial network (GAN) the generator and the discriminator. In this tutorial, we will combine the generator and discriminator and start training the GAN.
Video Tutorials
NIPS 2016 Tutorial: Generative Adversarial Networks
This tutorial presented by Ian Goodfellow at NIPS 2016 on generative adversarial networks. The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs compare to other generative models, (3) the details of how GANs work, (4) research frontiers in GANs, and (5) state-of-the-art image models that combine GANs with other methods. Finally, the tutorial contains three exercises for readers to complete, and the solutions to these exercises.