Abstract
Current Domain Adaptation (DA) methods based on
deep architectures assume that the source samples arise
from a single distribution. However, in practice most
datasets can be regarded as mixtures of multiple domains.
In these cases exploiting single-source DA methods for
learning target classifiers may lead to sub-optimal, if not
poor, results. In addition, in many applications it is difficult
to manually provide the domain labels for all source data
points, i.e. latent domains should be automatically discovered. This paper introduces a novel Convolutional Neural
Network (CNN) architecture which (i) automatically discovers latent domains in visual datasets and (ii) exploits this
information to learn robust target classifiers. Our approach
is based on the introduction of two main components, which
can be embedded into any existing CNN architecture: (i) a
side branch that automatically computes the assignment of
a source sample to a latent domain and (ii) novel layers that
exploit domain membership information to appropriately
align the distribution of the CNN internal feature representations to a reference distribution. We test our approach
on publicly-available datasets, showing that it outperforms
state-of-the-art multi-source DA methods by a large margin