  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.
