A Generative Adversarial Network (GAN) consists of two sub-networks: (1) generator and (2) discriminator.
What does a discriminator should be able to do? Or more specifically, should it be able to distinguish (classify) a real object (for example a vector) from a generated one or should it be able to distinguish a set of generated vectors from a set of real vectors?
I tend to think that the second option is correct. However, if it is the case, how do we build a neural network that classifies a set of vectors instead of a vector?