  This work provides a framework for addressing the problem of supervised
domain adaptation with deep models. The main idea is to exploit adversarial
learning to learn an embedded subspace that simultaneously maximizes the
confusion between two domains while semantically aligning their embedding. The
supervised setting becomes attractive especially when there are only a few
target data samples that need to be labeled. In this few-shot learning
scenario, alignment and separation of semantic probability distributions is
difficult because of the lack of data. We found that by carefully designing a
training scheme whereby the typical binary adversarial discriminator is
augmented to distinguish between four different classes, it is possible to
effectively address the supervised adaptation problem. In addition, the
approach has a high speed of adaptation, i.e. it requires an extremely low
number of labeled target training samples, even one per category can be
effective. We then extensively compare this approach to the state of the art in
domain adaptation in two experiments: one using datasets for handwritten digit
recognition, and one using datasets for visual object recognition.
