  Transfer learning aims at improving the performance of target learners on
target domains by transferring the knowledge contained in different but related
source domains. In this way, the dependence on a large number of target domain
data can be reduced for constructing target learners. Due to the wide
application prospects, transfer learning has become a popular and promising
area in machine learning. Although there are already some valuable and
impressive surveys on transfer learning, these surveys introduce approaches in
a relatively isolated way and lack the recent advances in transfer learning.
Due to the rapid expansion of the transfer learning area, it is both necessary
and challenging to comprehensively review the relevant studies. This survey
attempts to connect and systematize the existing transfer learning researches,
as well as to summarize and interpret the mechanisms and the strategies of
transfer learning in a comprehensive way, which may help readers have a better
understanding of the current research status and ideas. Unlike previous
surveys, this survey paper reviews more than forty representative transfer
learning approaches, especially homogeneous transfer learning approaches, from
the perspectives of data and model. The applications of transfer learning are
also briefly introduced. In order to show the performance of different transfer
learning models, over twenty representative transfer learning models are used
for experiments. The models are performed on three different datasets, i.e.,
Amazon Reviews, Reuters-21578, and Office-31. And the experimental results
demonstrate the importance of selecting appropriate transfer learning models
for different applications in practice.
