Artifical neural nets have been all over the news in the last few weeks—they dream, nightmare-ify music videos, and now they can beat you in Pictionary.
Researchers at the Queen Mary University of London designed their neural network to analyze drawings on varying levels of abstraction, and figure out what is depicted. This technology could have wide use in shopping, criminal identification, and even finding the exact picture of a cat you're looking for on the internet.
The program, called Sketch-a-Net,beat a human sketch recognition benchmark by a very close margin of 74.9 percent recognition to 73.1 percent for humans. It uses eight layers of
processing to break a sketch apart.
Like other neural networks, the Queen Mary team had to train Sketch-a-Net by showing it a variety of human sketches matched with keywords. To ensure the computer knew a variety of sketch styles and figures, researchers used the Technical University of Berlin sketch dataset, the largest set of its kind at 20,000 sketches. The TU-Berlin team originally sourced the sketches from Amazon's Mechanical Turk, an anonymous horde of internet task-doers, who sketched 80 objects in 250 categories.