  We introduce two criteria to regularize the optimization involved in learning
a classifier in a domain where no annotated data are available, leveraging
annotated data in a different domain, a problem known as unsupervised domain
adaptation. We focus on the task of semantic segmentation, where annotated
synthetic data are aplenty, but annotating real data is laborious. The first
criterion, inspired by visual psychophysics, is that the map between the two
image domains be phase-preserving. This restricts the set of possible learned
maps, while enabling enough flexibility to transfer semantic information. The
second criterion aims to leverage ecological statistics, or regularities in the
scene which are manifest in any image of it, regardless of the characteristics
of the illuminant or the imaging sensor. It is implemented using a deep neural
network that scores the likelihood of each possible segmentation given a single
un-annotated image. Incorporating these two priors in a standard domain
adaptation framework improves performance across the board in the most common
unsupervised domain adaptation benchmarks for semantic segmentation.
