Abstract—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. As 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 in
a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Different from
previous surveys, this survey paper reviews over forty representative 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, 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.
