  Unsupervised Domain Adaptation (UDA) refers to the problem of learning a
model in a target domain where labeled data are not available by leveraging
information from annotated data in a source domain. Most deep UDA approaches
operate in a single-source, single-target scenario, i.e. they assume that the
source and the target samples arise from a single distribution. However, in
practice most datasets can be regarded as mixtures of multiple domains. In
these cases, exploiting traditional single-source, single-target methods for
learning classification models may lead to poor results. Furthermore, it is
often difficult to provide the domain labels for all data points, i.e. latent
domains should be automatically discovered. This paper introduces a novel deep
architecture which addresses the problem of UDA by automatically discovering
latent domains in visual datasets and exploiting this information to learn
robust target classifiers. Specifically, our architecture is based on two main
components, i.e. a side branch that automatically computes the assignment of
each sample to its latent domain and novel layers that exploit domain
membership information to appropriately align the distribution of the CNN
internal feature representations to a reference distribution. We evaluate our
approach on publicly available benchmarks, showing that it outperforms
state-of-the-art domain adaptation methods.
