The generative-adversarial network consists of two parts: generative and discriminative. The generative neural network creates samples, and the discriminative tries to distinguish correct samples from incorrect ones.
Just imagine GAN as a counterfeiter and a policeman competing with each other. The counterfeiter learns to make simulated bills, and the policeman learns to detect them. Both are dynamic. The policeman also trains, and each side comes to learn the methods of the other in a constant escalation.