To understand GANs, we need to understand two terms: generator and discriminator. First, we should have a reasonable sample of images of an object. A generative network (generator) learns representation from a sample of images and then generates images similar to the sample of images. A discriminator network (discriminator) is one that looks at the image generated (by the generator network) and the original sample of images and classifies images as original ones or generated (fake) ones.
The generator network generates images in such a way that the discriminator classifies the images as real ones. The discriminator network classifies the generated images as fake and the images in the original sample as real.
Essentially, the adversarial term in GAN represents the opposite nature of the two networks—a generator network, which generates images to fool the discriminator network, and a discriminator network that classifies each image by saying whether the image is...
