Learn in the textbookGenerative Models and Large Language Models
DefinitionA GAN is a deep learning model that trains a generator, which makes fake data, against a discriminator, which tries to tell it apart from real data, until it can create new data that resembles the real thing.
ExplanationThe generator builds data from random numbers, and the discriminator compares real data with the generator's output to judge which is which. Based on each verdict, the generator adjusts to become harder to catch, while the discriminator adjusts to catch it more easily. Through this repeated contest, the generator learns to produce data that is hard to tell from the real thing.
AnalogyPicture a forger and an art appraiser locked in a contest: the sharper the appraiser's eye becomes, the closer the forger's paintings get to the genuine article.
Memory hookThe letters stand for G = Generative, A = Adversarial, N = Network, so the three letters alone let you rebuild the full name.