Which Face is Real? Applying StyleGAN to Create Fake People - KDnuggets

A Generative model aims to learn and understand a dataset's true distribution and create new data from it using unsupervised learning. These models (such as StyleGAN) have had mixed success as it is quite difficult to understand the complexities of certain probability distributions. In order to sidestep these roadblocks, The Adversarial Nets Framework was created whereby the generative model is pitted against an adversary: a discriminative model that learns to determine whether a sample is from the model distribution or the data distribution. The generative model generates samples by passing random noise through a multilayer perceptron, and the discriminative model is also a multilayer perceptron. We refer to this case as Adversarial Nets.