  Although deep networks have significantly increased the performance of visual
recognition methods, it is still challenging to achieve the robustness across
visual domains that is necessary for real-world applications. To tackle this
issue, research on domain adaptation and generalization has flourished over the
last decade. An important aspect to consider when assessing the work done in
the literature so far is the amount of data annotation necessary for training
each approach, both at the source and target level. In this paper we argue that
the data annotation overload should be minimal, as it is costly. Hence, we
propose to use self-supervised learning to achieve domain generalization and
adaptation. We consider learning regularities from non annotated data as an
auxiliary task, and cast the problem within an Auxiliary Learning principled
framework. Moreover, we suggest to further exploit the ability to learn about
visual domains from non annotated images by learning from target data while
testing, as data are presented to the algorithm one sample at a time. Results
on three different scenarios confirm the value of our approach.
