  Neural networks are usually over-parameterized with significant redundancy in
the number of required neurons which results in unnecessary computation and
memory usage at inference time. One common approach to address this issue is to
prune these big networks by removing extra neurons and parameters while
maintaining the accuracy. In this paper, we propose NoiseOut, a fully automated
pruning algorithm based on the correlation between activations of neurons in
the hidden layers. We prove that adding additional output neurons with entirely
random targets results into a higher correlation between neurons which makes
pruning by NoiseOut even more efficient. Finally, we test our method on various
networks and datasets. These experiments exhibit high pruning rates while
maintaining the accuracy of the original network.
