I've been reading about the importance of Generative Adversarial Networks (GANs), and I would like to double check that I understood correctly why they are so relevant.
Before GANs, what people did was to train a neural network and use a set of functions for classification/prediction purposes. The innovation introduced by GANs is that the model is converted from a set of functions into an object, which could be an image or something similar. Then the similarity of the input with this model is evaluated.
I'm not sure why this approach is groundbreaking: we still make a model and we evaluate how close the input is to it. Am I missing something?