We have already explored two types of generative models: RBMs and DBNs. In this chapter, we will explore generative adversarial networks (GANs), one of the latest and most promising areas of unsupervised learning and generative modeling.
GANs were introduced by Ian Goodfellow and his fellow researchers at the University of Montreal in 2014. In GANs, we have two neural networks. One network known as the generator generates data based on a model it has created using samples of real data it has received as input. The other network known as the discriminator discriminates between the data created by the generator and data from the true distribution.
As a simple analogy, the generator is the counterfeiter, and the discriminator is the police trying to identify the forgery. The two networks are locked in a zero-sum game. The generator is trying to fool the discriminator into thinking the synthetic data comes from the true distribution, and the discriminator is trying to call out the synthetic data as fake.
GANs are unsupervised learning algorithms because the generator can learn the underlying structure of the true distribution even when there are no labels. The generator learns the underlying structure by using a number of parameters significantly smaller than the amount of data it has trained on—a core concept of unsupervised learning that we have explored many times in previous chapters. This constraint forces the generator ...