  Human adaptability relies crucially on learning and merging knowledge from
both supervised and unsupervised tasks: the parents point out few important
concepts, but then the children fill in the gaps on their own. This is
particularly effective, because supervised learning can never be exhaustive and
thus learning autonomously allows to discover invariances and regularities that
help to generalize. In this paper we propose to apply a similar approach to the
problem of object recognition across domains: our model learns the semantic
labels in a supervised fashion, and broadens its understanding of the data by
learning from self-supervised signals on the same images. This secondary task
helps the network to focus on object shapes, learning concepts like spatial
orientation and part correlation, while acting as a regularizer for the
classification task over multiple visual domains. Extensive experiments confirm
our intuition and show that our multi-task method combining supervised and
self-supervised knowledge shows competitive results with respect to more
complex domain generalization and adaptation solutions. It also proves its
potential in the novel and challenging predictive and partial domain adaptation
scenarios.
