  As a new classification platform, deep learning has recently received
increasing attention from researchers and has been successfully applied to many
domains. In some domains, like bioinformatics and robotics, it is very
difficult to construct a large-scale well-annotated dataset due to the expense
of data acquisition and costly annotation, which limits its development.
Transfer learning relaxes the hypothesis that the training data must be
independent and identically distributed (i.i.d.) with the test data, which
motivates us to use transfer learning to solve the problem of insufficient
training data. This survey focuses on reviewing the current researches of
transfer learning by using deep neural network and its applications. We defined
deep transfer learning, category and review the recent research works based on
the techniques used in deep transfer learning.
