In a generative adversarial network (GAN) involves two deep neural networks: one attempts to generate a realistic image (or other media), and the other acts as a critic deterining if the image looks real. The effect is rather like the methd used in game playing by which networks play against each other and learn to be better and better. As the generator network imporves the critic has to learn to make finer distinctions, which then forces the generator to learn to be yet better.
Defined on page 279
Used on Chap. 8: page 158; Chap. 12: page 279; Chap. 21: page 523
Also known as GAN