Learning to Generalize One Sample at a Time with Self-Supervision

Antonio D'Innocente, Silvia Bucci, Tatiana Tommasi, Barbara Caputo

Abstract:

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.