  Transfer learning aims at transferring knowledge from a well-labeled domain
to a similar but different domain with limited or no labels. Unfortunately,
existing learning-based methods often involve intensive model selection and
hyperparameter tuning to obtain good results. Moreover, cross-validation is not
possible for tuning hyperparameters since there are often no labels in the
target domain. This would restrict wide applicability of transfer learning
especially in computationally-constraint devices such as wearables. In this
paper, we propose a practically Easy Transfer Learning (EasyTL) approach which
requires no model selection and hyperparameter tuning, while achieving
competitive performance. By exploiting intra-domain structures, EasyTL is able
to learn both non-parametric transfer features and classifiers. Extensive
experiments demonstrate that, compared to state-of-the-art traditional and deep
methods, EasyTL satisfies the Occam's Razor principle: it is extremely easy to
implement and use while achieving comparable or better performance in
classification accuracy and much better computational efficiency. Additionally,
it is shown that EasyTL can increase the performance of existing transfer
feature learning methods.
