  While invaluable for many computer vision applications, decomposing a natural
image into intrinsic reflectance and shading layers represents a challenging,
underdetermined inverse problem. As opposed to strict reliance on conventional
optimization or filtering solutions with strong prior assumptions, deep
learning based approaches have also been proposed to compute intrinsic image
decompositions when granted access to sufficient labeled training data. The
downside is that current data sources are quite limited, and broadly speaking
fall into one of two categories: either dense fully-labeled images in
synthetic/narrow settings, or weakly-labeled data from relatively diverse
natural scenes. In contrast to many previous learning-based approaches, which
are often tailored to the structure of a particular dataset (and may not work
well on others), we adopt core network structures that universally reflect
loose prior knowledge regarding the intrinsic image formation process and can
be largely shared across datasets. We then apply flexibly supervised loss
layers that are customized for each source of ground truth labels. The
resulting deep architecture achieves state-of-the-art results on all of the
major intrinsic image benchmarks, and runs considerably faster than most at
test time.
