Am I crazy, or is the short-run benefit more for unsupervised feature learning more than classification accuracy? If I recall from my brief reading on the subject, kernel-aided SVMs and Random Forests are basically equivalent to a three-layer deep graphical net, but on painfully feature-engineered inputs. I'd wager learning features is more useful now than any benefit of a super-deep architecture.

(I would love for a practitioner to shoot this down or corroborate my hunch-- awfully new to neural nets, having basically read a monograph, played with Theano, and watched some Hinton lectures.)