  Transfer learning (TL) leverages previously obtained knowledge to learn new
tasks efficiently and has been used to train deep learning (DL) models with
limited amount of data. When TL is applied to DL, pretrained (teacher) models
are fine-tuned to build domain specific (student) models. This fine-tuning
relies on the fact that DL model can be decomposed to classifiers and feature
extractors, and a line of studies showed that the same feature extractors can
be used to train classifiers on multiple tasks. Furthermore, recent studies
proposed multiple algorithms that can fine-tune teacher models' feature
extractors to train student models more efficiently. We note that regardless of
the fine-tuning of feature extractors, the classifiers of student models are
trained with final outputs of feature extractors (i.e., the outputs of
penultimate layers). However, a recent study suggested that feature maps in
ResNets across layers could be functionally equivalent, raising the possibility
that feature maps inside the feature extractors can also be used to train
student models' classifiers. Inspired by this study, we tested if feature maps
in the hidden layers of the teacher models can be used to improve the student
models' accuracy (i.e., TL's efficiency). Specifically, we developed 'adaptive
transfer learning (ATL)', which can choose an optimal set of feature maps for
TL, and tested it in the few-shot learning setting. Our empirical evaluations
suggest that ATL can help DL models learn more efficiently, especially when
available examples are limited.
