Generating Faces with Deconvolution Networks

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. I happened to stumble upon the Radboud Faces Database some time ago, and wondered if something like this could be used to generate and interpolate between faces as well. To implement this, I adapted a version of the "1s-S-deep" model from the chairs paper.