Rewriting the rules of machine-generated art

Horses don't normally wear hats, and deep generative models, or GANs, don't normally follow rules laid out by human programmers. But a new tool developed at MIT lets anyone go into a GAN and tell the model, like a coder, to put hats on the heads of the horses it draws. In a new study appearing at the European Conference on Computer Vision this month, researchers show that the deep layers of neural networks can be edited, like so many lines of code, to generate surprising images no one has seen before. "GANs are incredible artists, but they're confined to imitating the data they see," says the study's lead author, David Bau, a Ph.D. student at MIT. "If we can rewrite the rules of a GAN directly, the only limit is human imagination." Generative adversarial networks, or GANs, pit two neural networks against each other to create hyper-realistic images and sounds. One neural network, the generator, learns to mimic the faces it sees in photos, or the words it hears spoken.