The portion of the GAN that trains the generator includes: random input generator network, which transforms the random input into a data instance discriminator network, which classifies the generated data discriminator output generator loss, which penalizes the generator for failing to fool the discriminator

generator part of a GAN learns to create fake data by incorporating feedback from the discriminator

generated instances become negative training examples for the discriminator