because it's learned to reject lots of fake data it actually gets regularize drooly well using this approach tim Salomon's and and i and our other collaborators in open area we're able to set the state of the art on several different recognition tasks with very few labeled examples on em nist c fart n @ sv hn another important research direction is learning to make the code interpretable Peter Chen's info Gann paper here at nips actually shows how we can learn a code where different elements of the code correspond to