Distilling the Knowledge in a Neural Network
Venue
NIPS Deep Learning and Representation Learning Workshop (2014)
Publication Year
2014
Authors
Geoffrey Hinton, Oriol Vinyals, Jeffrey Dean
BibTeX
Abstract
A very simple way to improve the performance of almost any machine learning
algorithm is to train many different models on the same data and then to average
their predictions. Unfortunately, making predictions using a whole ensemble of
models is cumbersome and may be too computationally expensive to allow deployment
to a large number of users, especially if the individual models are large neural
nets. Caruana and his collaborators have shown that it is possible to compress the
knowledge in an ensemble into a single model which is much easier to deploy and we
develop this approach further using a different compression technique. We achieve
some surprising results on MNIST and we show that we can significantly improve the
acoustic model of a heavily used commercial system by distilling the knowledge in
an ensemble of models into a single model. We also introduce a new type of ensemble
composed of one or more full models and many specialist models which learn to
distinguish fine-grained classes that the full models confuse. Unlike a mixture of
experts, these specialist models can be trained rapidly and in parallel.
