  This work provides a unified framework for addressing the problem of visual
supervised domain adaptation and generalization with deep models. The main idea
is to exploit the Siamese architecture to learn an embedding subspace that is
discriminative, and where mapped visual domains are semantically aligned and
yet maximally separated. The supervised setting becomes attractive especially
when only few target data samples need to be labeled. In this scenario,
alignment and separation of semantic probability distributions is difficult
because of the lack of data. We found that by reverting to point-wise
surrogates of distribution distances and similarities provides an effective
solution. In addition, the approach has a high speed of adaptation, which
requires an extremely low number of labeled target training samples, even one
per category can be effective. The approach is extended to domain
generalization. For both applications the experiments show very promising
results.
