  Deep networks are nowadays becoming popular in many computer vision and
pattern recognition tasks. Among these networks, deep kernels are particularly
interesting and effective, however, their computational complexity is a major
issue especially on cheap hardware resources. In this paper, we address the
issue of efficient computation in deep kernel networks. We propose a novel
framework that reduces dramatically the complexity of evaluating these deep
kernels. Our method is based on a coarse-to-fine cascade of networks designed
for efficient computation; early stages of the cascade are cheap and reject
many patterns efficiently while deep stages are more expensive and accurate.
The design principle of these reduced complexity networks is based on a variant
of the cross-entropy criterion that reduces the complexity of the networks in
the cascade while preserving all the positive responses of the original kernel
network. Experiments conducted - on the challenging and time demanding change
detection task, on very large satellite images - show that our proposed
coarse-to-fine approach is effective and highly efficient.
