Art for the AI generation Art and Artificial Intelligence Laboratory, Rutgers University
Generative Adversarial Network for Painting
June 29, 2017. The
full paper, by Chris Baraniuk, in Daily News
The team – which also included researchers at Rutgers University in New Jersey and Facebook’s AI lab in California – modified a type of algorithm known as a generative adversarial network (GAN), in which two neural nets play off against each other to get better and better results. One creates a solution, the other judges it – and the algorithm loops back and forth until the desired result is reached.
In the art AI, one of these roles is played by a generator network, which creates images. The other is played by a discriminator network, which was trained on 81,500 paintings to tell the difference between images we would class as artworks and those we wouldn’t – such as a photo or diagram, say.
The discriminator was also trained to distinguish different styles of art,
such as rococo or cubism.
Art with a twist
The clever twist is that the generator is primed to produce an image that the discriminator recognises as art, but which does not fall into any of the existing styles.
“You want to have something really creative and striking – but at the same time not go too far and make something that isn’t aesthetically pleasing,” says team member Ahmed Elgammal at Rutgers University.