  Deep neural networks include millions of learnable parameters, making their
deployment over resource-constrained devices problematic. SeReNe
(Sensitivity-based Regularization of Neurons) is a method for learning sparse
topologies with a structure, exploiting neural sensitivity as a regularizer. We
define the sensitivity of a neuron as the variation of the network output with
respect to the variation of the activity of the neuron. The lower the
sensitivity of a neuron, the less the network output is perturbed if the neuron
output changes. By including the neuron sensitivity in the cost function as a
regularization term, we areable to prune neurons with low sensitivity. As
entire neurons are pruned rather then single parameters, practical network
footprint reduction becomes possible. Our experimental results on multiple
network architectures and datasets yield competitive compression ratios with
respect to state-of-the-art references.
