Tackling Partial Domain Adaptation with Self-Supervision

Silvia Bucci, Antonio D’Innocente, Tatiana Tommasi

Abstract:

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.