Our main goal is for the generator to produce realistic images, and the GAN framework is a vehicle for that goal. We'll train the generator and the discriminator separately and sequentially (one after the other) and alternate between the two phases multiple times.
Before going into more detail, let's use the following diagram to introduce some notations:
- We'll denote the generator with
, where 
is the network weights and z is the latent vector, which serves as an input to the generator. Think of it as a random seed value to kickstart ...
