Adventures with Artificial Intelligence Part 1
Adventures with Artificial Intelligence Part 2
Adventures with Artificial Intelligence Part 3
Made with the ArtBreeder Generative Adversarial Network (GAN) created by Joel Simon.
A basic GAN is composed of two separate neural networks which are in continual competition against each other (adversaries).
One of these, called the generator, is tasked with the generation of new data instances that it creates from random noise, while the other, called a discriminator, evaluates these generated instances for authenticity.
Both tasks are phases in the GAN’s process cycle and are interdependent on each other. The generative phase is influenced by the discriminative phase’s evaluation, and the discriminative phase makes comparisons between the original dataset and the generated samples.
As training progresses, both networks keep getting smarter—the generator at generating fake images and the discriminator at detecting their authenticity. By the time the model has been trained, the generator manages to create an image authentic enough that the discriminator can’t tell if it’s a fake or not. Often, this final generated image is the resulting output.