In this section, we'll talk about arguably the most popular generative model today: the GAN framework. It was first introduced in 2014 in the landmark paper Generative Adversarial Nets (http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf). The GAN framework can work with any type of data, but its most popular application by far is to generate images, and we'll discuss them in this context only. Let's see how it works:
A GAN system
A GAN is a system of two components (neural networks):
- Generator: This is the generative model itself. It takes a probability distribution (random noise) as input and tries to generate a realistic output image. Its purpose is similar to the decoder part of the VAE.
- Discriminator: This takes two alternating inputs: real images of the training dataset or generated fake samples from the generator. It tries...
