  Domain adaptation (DA) aims at improving the performance of a model on target
domains by transferring the knowledge contained in different but related source
domains. With recent advances in deep learning models which are extremely data
hungry, the interest for visual DA has significantly increased in the last
decade and the number of related work in the field exploded. The aim of this
paper, therefore, is to give a comprehensive overview of deep domain adaptation
methods for computer vision applications. First, we detail and compared
different possible ways of exploiting deep architectures for domain adaptation.
Then, we propose an overview of recent trends in deep visual DA. Finally, we
mention a few improvement strategies, orthogonal to these methods, that can be
applied to these models. While we mainly focus on image classification, we give
pointers to papers that extend these ideas for other applications such as
semantic segmentation, object detection, person re-identifications, and others.
