  Transfer learning is a valuable tool in deep learning as it allows
propagating information from one "source dataset" to another "target dataset",
especially in the case of a small number of training examples in the latter.
Yet, discrepancies between the underlying distributions of the source and
target data are commonplace and are known to have a substantial impact on
algorithm performance. In this work we suggest novel information-theoretic
approaches for the analysis of the performance of deep neural networks in the
context of transfer learning. We focus on the task of semi-supervised transfer
learning, in which unlabeled samples from the target dataset are available
during network training on the source dataset. Our theory suggests that one may
improve the transferability of a deep neural network by incorporating
regularization terms on the target data based on information-theoretic
quantities, namely the Mutual Information and the Lautum Information. We
demonstrate the effectiveness of the proposed approaches in various
semi-supervised transfer learning experiments.
