  In this paper we focus on the spatial nature of visual domain shift,
attempting to learn where domain adaptation originates in each given image of
the source and target set. We borrow concepts and techniques from the CNN
visualization literature, and learn domainnes maps able to localize the degree
of domain specificity in images. We derive from these maps features related to
different domainnes levels, and we show that by considering them as a
preprocessing step for a domain adaptation algorithm, the final classification
performance is strongly improved. Combined with the whole image representation,
these features provide state of the art results on the Office dataset.
