Photos from Crude Sketches: Nvidia’s GauGAN Explained Visually

The idea is to generate images using two neural networks: a generator, and an image classifier (a discriminator). The discriminator is tasked with distinguishing the generator’s output images from real images from the dataset (its classes are “fake” and “real”), while the generator’s job is to fool the discriminator by producing images that look like those in the dataset. Its discriminator $D$ takes an image $\vec{x}$ and outputs a single value $D(\vec{x})$ between 0 and 1, representing its confidence that $\vec{x}$ is from the dataset rather than a fake image from the generator.

Source: adamdking.com