Blippar, the AR (augmented reality) startup aiming to build a visual catalog of every object in the world, has added real-time facial recognition technology to its mobile app, allowing users to create “Augmented Reality Face Profiles.”
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One of my favorite deep learning papers is Learning to Generate Chairs, Tables, and Cars with Convolutional Networks. It’s a very simple concept – you give the network the parameters of the thing you want to draw and it does it – but it yields an incredibly interesting result.
The network seems like it is able to learn concepts about 3D space and the structure of the objects it’s drawing, and because it’s generating images rather than numbers it gives us a better sense about how the network “thinks” as well.
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PIXELATION HAS LONG been a familiar fig leaf to cover our visual media’s most private parts. Blurred chunks of text or obscured faces and license plates show up on the news, in redacted documents, and online. The technique is nothing fancy, but it has worked well enough, because people can’t see or read through the distortion. The problem, however, is that as computer vision becomes increasingly robust, it’s starting to see things we can’t.
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