To do so, thousands of photos of faces from Flickr were fed to a type of neural network technique called a “generative adversarial network” (GAN). GANs work by using two neural networks playing an adversarial game: one (the “generator”) tries to generate increasingly convincing output, while a the second (the “critic”) tries to learn to distinguish real photos from generated ones.

At first both networks are poor at their respective tasks. But as the discriminator network starts to learn to predict fake from real, it keeps the generator on its toes, pushing it to generate harder and harder examples. As the generator gets better the discriminator also has to improve in turn, in order to keep up. With time, the generated output becomes increasingly realistic, as both adversaries try to outwit each other.

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