Incidentally, that's almost exactly how generative adversarial networks work.

To generate realistic images, you make two neural networks: one of them (D) takes an image as input and decides whether it's real or whether it's the output of (G). The other (G) takes random noise as input and turns it into an image that will fool the (D) network.

Make them fight until they both get strong, and then use (G) as the final model.