  Domain adaptation approaches have shown promising results in reducing the
marginal distribution difference among visual domains. They allow to train
reliable models that work over datasets of different nature (photos, paintings
etc), but they still struggle when the domains do not share an identical label
space. In the partial domain adaptation setting, where the target covers only a
subset of the source classes, it is challenging to reduce the domain gap
without incurring in negative transfer. Many solutions just keep the standard
domain adaptation techniques by adding heuristic sample weighting strategies.
In this work we show how the self-supervisory signal obtained from the spatial
co-location of patches can be used to define a side task that supports
adaptation regardless of the exact label sharing condition across domains. We
build over a recent work that introduced a jigsaw puzzle task for domain
generalization: we describe how to reformulate this approach for partial domain
adaptation and we show how it boosts existing adaptive solutions when combined
with them. The obtained experimental results on three datasets supports the
effectiveness of our approach.
