  Learning from synthetic data has many important and practical applications.
An example of application is photo-sketch recognition. Using synthetic data is
challenging due to the differences in feature distributions between synthetic
and real data, a phenomenon we term synthetic gap. In this paper, we
investigate and formalize a general framework-Stacked Multichannel Autoencoder
(SMCAE) that enables bridging the synthetic gap and learning from synthetic
data more efficiently. In particular, we show that our SMCAE can not only
transform and use synthetic data on the challenging face-sketch recognition
task, but that it can also help simulate real images, which can be used for
training classifiers for recognition. Preliminary experiments validate the
effectiveness of the framework.
