  In this paper, we propose a new method called ProfWeight for transferring
information from a pre-trained deep neural network that has a high test
accuracy to a simpler interpretable model or a very shallow network of low
complexity and a priori low test accuracy. We are motivated by applications in
interpretability and model deployment in severely memory constrained
environments (like sensors). Our method uses linear probes to generate
confidence scores through flattened intermediate representations. Our transfer
method involves a theoretically justified weighting of samples during the
training of the simple model using confidence scores of these intermediate
layers. The value of our method is first demonstrated on CIFAR-10, where our
weighting method significantly improves (3-4%) networks with only a fraction of
the number of Resnet blocks of a complex Resnet model. We further demonstrate
operationally significant results on a real manufacturing problem, where we
dramatically increase the test accuracy of a CART model (the domain standard)
by roughly 13%.
