  Classifiers trained on given databases perform poorly when tested on data
acquired in different settings. This is explained in domain adaptation through
a shift among distributions of the source and target domains. Attempts to align
them have traditionally resulted in works reducing the domain shift by
introducing appropriate loss terms, measuring the discrepancies between source
and target distributions, in the objective function. Here we take a different
route, proposing to align the learned representations by embedding in any given
network specific Domain Alignment Layers, designed to match the source and
target feature distributions to a reference one. Opposite to previous works
which define a priori in which layers adaptation should be performed, our
method is able to automatically learn the degree of feature alignment required
at different levels of the deep network. Thorough experiments on different
public benchmarks, in the unsupervised setting, confirm the power of our
approach.
