Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
I've only recently started reading about deep neural networks, and the thing that strikes me the most about the literature is the <i>lack of mathematics</i>.<p>Open a NIPS paper from 2010 or so, and you'll see extremely dense mathematics: nonparametrics, variational approximation, sampling theory, riemannian geometry. But from my (admittedly small) sampling of the convnet / RNN literature there really doesn't seem to be much maths there. The typical paper seems to run along the lines of "We tried this, and it worked".<p>I'm not sure whether there's anything to learn from this observation, but I think it's striking all the same.