  Deep learning (DL) has been the primary approach used in various computer
vision tasks due to its relevant results achieved on many tasks. However, on
real-world scenarios with partially or no labeled data, DL methods are also
prone to the well-known domain shift problem. Multi-source unsupervised domain
adaptation (MSDA) aims at learning a predictor for an unlabeled domain by
assigning weak knowledge from a bag of source models. However, most works
conduct domain adaptation leveraging only the extracted features and reducing
their domain shift from the perspective of loss function designs. In this
paper, we argue that it is not sufficient to handle domain shift only based on
domain-level features, but it is also essential to align such information on
the feature space. Unlike previous works, we focus on the network design and
propose to embed Multi-Source version of DomaIn Alignment Layers (MS-DIAL) at
different levels of the predictor. These layers are designed to match the
feature distributions between different domains and can be easily applied to
various MSDA methods. To show the robustness of our approach, we conducted an
extensive experimental evaluation considering two challenging scenarios: digit
recognition and object classification. The experimental results indicated that
our approach can improve state-of-the-art MSDA methods, yielding relative gains
of up to +30.64% on their classification accuracies.
