Vanilla GAN on MNIST

Key Insight

A GAN trains two networks in a contest: a generator that turns a vector of random noise into a fake image, and a discriminator that looks at an image and guesses whether it is real or generated. They improve by competing — the generator keeps trying to fool the critic while the critic keeps getting better at catching it — like a counterfeiter and a detective who each sharpen the other. This project builds DCGAN, the first convolutional GAN recipe that trained reliably, on MNIST digits, where you will watch the two losses oscillate instead of settling and learn to spot mode collapse — when the generator gives up on variety and keeps emitting the same one or two digits that happen to fool the critic.