Basic

The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes “fake” data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it’s received is real or fake.

The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data.

The discriminator is a classifier that is trained to figure out which data is real and which is fake.