  The availability of abundant labeled data in recent years led the researchers
to introduce a methodology called transfer learning, which utilizes existing
data in situations where there are difficulties in collecting new annotated
data. Transfer learning aims to boost the performance of a target learner by
applying another related source data. In contrast to the traditional machine
learning and data mining techniques, which assume that the training and testing
data lie from the same feature space and distribution, transfer learning can
handle situations where there is a discrepancy between domains and
distributions. These characteristics give the model the potential to utilize
the available related source data and extend the underlying knowledge to the
target task achieving better performance. This survey paper aims to give a
concise review of traditional and current transfer learning settings, existing
challenges, and related approaches.
