What you describe is exactly the way these models work! Typically; a GAN (Generative Adversarial Network) consists of (1) the generator; a model generating ima

What you describe is exactly the way these models work! Typically; a GAN (Generative Adversarial Network) consists of (1) the generator; a model generating images and (2) the discriminator; a model that learns whether images it is fed come from the generator or from the image dataset. The (gradient) information of how the discriminator made its decision is fed back into the generator, in order to help it learn how to generate more _real_ images. The discriminator is what you describe in step 1, and the generator is your step 2.