Generative Adversarial Networks (GANs) consist of two networks: a generator G(z) and a discriminator D(x). The discriminator is trying to distinguish real objects from objects produced by a generator.
Generative adversarial networks (GANs) are powerful gen-erative models, but usually suffer from instability and generalization problem which may lead to poor generations.
