  Transferring knowledge from a teacher neural network pretrained on the same
or a similar task to a student neural network can significantly improve the
performance of the student neural network. Existing knowledge transfer
approaches match the activations or the corresponding hand-crafted features of
the teacher and the student networks. We propose an information-theoretic
framework for knowledge transfer which formulates knowledge transfer as
maximizing the mutual information between the teacher and the student networks.
We compare our method with existing knowledge transfer methods on both
knowledge distillation and transfer learning tasks and show that our method
consistently outperforms existing methods. We further demonstrate the strength
of our method on knowledge transfer across heterogeneous network architectures
by transferring knowledge from a convolutional neural network (CNN) to a
multi-layer perceptron (MLP) on CIFAR-10. The resulting MLP significantly
outperforms the-state-of-the-art methods and it achieves similar performance to
the CNN with a single convolutional layer.
