  We propose a new and completely data-driven approach for generating a
photo-consistent image transform. We show that simple classical algorithms
which operate in the transform domain become extremely resilient to
illumination changes. This considerably improves matching accuracy,
outperforming the use of state-of-the-art invariant representations as well as
new matching methods based on deep features. The transform is obtained by
training a neural network with a specialized triplet loss, designed to
emphasize actual scene changes while attenuating illumination changes. The
transform yields an illumination invariant representation, structured as an
image map, which is highly flexible and can be easily used for various tasks.
