The architecture of a GAN is shown in the following diagram:

As shown in the preceding diagram, Generator takes the random noise, , as input by sampling from a uniform or normal distribution and generates a fake image by implicitly learning the distribution of the training set.

We sample an image, , from the real data distribution, , and fake data distribution, , and feed it to the discriminator, . We feed real and fake images to the discriminator and the discriminator performs a binary classification task. That is, it returns 0 when the image is fake and 1 when the image is real.