  The empirical fact that classifiers, trained on given data collections,
perform poorly when tested on data acquired in different settings is
theoretically explained in domain adaptation through a shift among
distributions of the source and target domains. Alleviating the domain shift
problem, especially in the challenging setting where no labeled data are
available for the target domain, is paramount for having visual recognition
systems working in the wild. As the problem stems from a shift among
distributions, intuitively one should try to align them. In the literature,
this has resulted in a stream of works attempting to align the feature
representations learned from the source and target domains. Here we take a
different route. Rather than introducing regularization terms aiming to promote
the alignment of the two representations, we act at the distribution level
through the introduction of \emph{DomaIn Alignment Layers} (\DIAL), able to
match the observed source and target data distributions to a reference one.
Thorough experiments on three different public benchmarks we confirm the power
of our approach.
