Intro to Generative Adversarial Networks (GANs)
Intro to Generative Adversarial Networks (GANs) – PyImageSearch
“There are two networks in a basic GAN architecture: the generator model and the discriminator model. GANs get the word “adversarial” in its name because the two networks are trained simultaneously and competing against each other, like in a zero-sum game such as chess.
The generator model generates new images. The goal of the generator is to generate images that look so real that it fools the discriminator. In the simplest GAN architecture for image synthesis, the input is typically random noise, and its output is a generated image.
The discriminator is just a binary image classifier which you should already be familiar with. Its job is to classify whether an image is real or fake…”