  Domain Adaptation methodologies have shown to effectively generalize from a
labeled source domain to a label scarce target domain. Previous research has
either focused on unlabeled domain adaptation without any target supervision or
semi-supervised domain adaptation with few labeled target examples per class.
On the other hand Positive-Unlabeled (PU-) Learning has attracted increasing
interest in the weakly supervised learning literature since in quite some real
world applications positive labels are much easier to obtain than negative
ones. In this work we are the first to introduce the challenge of
Positive-Unlabeled Domain Adaptation where we aim to generalise from a fully
labeled source domain to a target domain where only positive and unlabeled data
is available. We present a novel two-step learning approach to this problem by
firstly identifying reliable positive and negative pseudo-labels in the target
domain guided by source domain labels and a positive-unlabeled risk estimator.
This enables us to use a standard classifier on the target domain in a second
step. We validate our approach by running experiments on benchmark datasets for
visual object recognition. Furthermore we propose real world examples for our
setting and validate our superior performance on parking occupancy data.
