Advances in Generative Adversarial Networks – BeyondMinds – Medium

Generative Adversarial Networks are a powerful class of neural networks with remarkable applications. They essentially consist of a system of two neural networks -- the Generator and the Discriminator -- dueling each other. Given a set of target samples, the Generator tries to produce samples that can fool the Discriminator into believing they are real. The Discriminator tries to resolve real (target) samples from fake (generated) samples. Using this iterative training approach, we eventually end up with a Generator that is really good at generating samples similar to the target samples. GANs have a plethora of applications, as they can learn to mimic data distributions of almost any kind.