  Deep domain adaption has emerged as a new learning technique to address the
lack of massive amounts of labeled data. Compared to conventional methods,
which learn shared feature subspaces or reuse important source instances with
shallow representations, deep domain adaption methods leverage deep networks to
learn more transferable representations by embedding domain adaptation in the
pipeline of deep learning. There have been comprehensive surveys for shallow
domain adaption, but few timely reviews the emerging deep learning based
methods. In this paper, we provide a comprehensive survey of deep domain
adaptation methods for computer vision applications with four major
contributions. First, we present a taxonomy of different deep domain adaption
scenarios according to the properties of data that define how two domains are
diverged. Second, we summarize deep domain adaption approaches into several
categories based on training loss, and analyze and compare briefly the
state-of-the-art methods under these categories. Third, we overview the
computer vision applications that go beyond image classification, such as face
recognition, semantic segmentation and object detection. Fourth, some potential
deficiencies of current methods and several future directions are highlighted.
