  Through this project, we researched on transfer learning methods and their
applications on real world problems. By implementing and modifying various
methods in transfer learning for our problem, we obtained an insight in the
advantages and disadvantages of these methods, as well as experiences in
developing neural network models for knowledge transfer. Due to time
constraint, we only applied a representative method for each major approach in
transfer learning. As pointed out in the literature review, each method has its
own assumptions, strengths and shortcomings. Thus we believe that an
ensemble-learning approach combining the different methods should yield a
better performance, which can be our future research focus.
