As seen with autoencoders, we can think of a general concept to create networks that will work together in a relationship, and training them will help us learn the latent spaces that allow us to generate new data samples.
Another type of generative network is GAN, where we have a generator model q(x|h) to map the small dimensional latent space of h (which is usually represented as noise samples from a simple distribution) to the input space of x. This is quite similar to the role of decoders in autoencoders.
The deal is now to introduce a discriminative model p(y| x), which tries to associate an input instance x to a yes/no binary answer y, about whether the generator model generated the input or was a genuine sample from the dataset ...
