We now have the perfect solution for celebrity obsession: an algorithm that conjures up new famous faces on demand.
Researchers at Nvidia created the celeb-generating algorithm using a clever new machine-learning technique. The faces are dreamed up using a more efficient version of what’s known as a generative adversarial network (or GAN).
A GAN consists of two neural networks, both trained using a particular data set. One network then tries to generate synthetic examples to fool the other network into thinking they came from the original data set. The process helps the first network improve its ability to produce realistic data.
GANs were invented by Google researcher Ian Goodfellow (who is also one of our 35 Innovators Under 35 for 2017), and they have proved remarkably effective for synthesizing realistic-sounding speech and all sorts of dazzling imagery. They could prove very useful for generating animated graphics for video games, and for compressing video more efficiently.
In a paper (PDF) submitted to an upcoming conference, the Nvidia researchers claim to have developed a better GAN by having it start off working with low-resolution images, and gradually increasing the image resolution as well as the size of the networks involved. They fed their GAN a data set of celebrity faces, and it produced some very realistic-looking faces (you can check out a video of the research here).
One thing to note, however, is that a few of the images feature strange artifacts and features, like a missing eyebrow or teeth in the wrong place—not exactly things that would get you a gig on reality TV. This goes to show that even if machine learning can produce amazing visual trickery, it lacks the deeper intelligence required to make sense of the real world.
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