Machine Learning Neural Network computer generated

Mike Tyka's “Portraits Of Imaginary People” is an experiment, where he is looking for new ways to use generative neural networks to make portraits of, well, as the title suggests, imaginary people. His approach combines multiple networks in different stages. The actual generation of the faces is restricted to a resolution of roughly 256 × 256 Pixels. In order to overcome this technical restriction of conventional neural networks, he upscales the output into higher resolution using multiple stages of machine learning methods, achieving printable pictures with a resolution of up to 4000 × 4000 Pixels. The aesthetic of the actual outcome is rough, haptic and has its very own quality, sometimes evoking associations with oil paintings or surrealism. Two things are to note: This is still a work-in-progress, an experiment with uncertain outcomes and the results are highly cherry-picked. Visit his page for more information.

On a side note: The results reminded me of a project called “Composite” by Brian Joseph Davis, where he generated police sketches of literary characters, by running their book description through composite sketch software used by law enforcement. Seeing the results and the implications of Tyka's experiments, you can see that law enforcement is also going to be changed by machine learning, computer vision and neural networks.

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