Generative Adversarial Nets

This paper introduces a new framework for estimating generative models via an adversarial process, in which two models are trained simultaneously: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The goal is to train the generative model to fool the discriminative model, ultimately leading to the generator producing realistic samples. This work laid the foundations for many subsequent advancements in generative modeling, especially in image synthesis, and is fundamental to understanding AI-powered design tools.