Source: KDNuggets, Jan 2017
a GAN as a new architecture for an unsupervised neural network able to achieve far better performance compared to traditional nets.
To be more precise GANs are a new way of training a neural net. GANs contain not one but two independent nets that work separately and act as adversaries (see the diagram below).
The first neural net is called the Discriminator (D) and is the net that has to undergo training. D is the classifier that will do the heavy lifting during the normal operation once the training is complete. The second network is called the Generator (G) and is tasked to generate random samples that resemble real samples with a twist rendering them as fake samples.