Towards thinner convolutional neural networks through Gradually Global Pruning
  Deep network pruning is an effective method to reduce the storage and
computation cost of deep neural networks when applying them to resource-limited
devices. Among many pruning granularities, neuron level pruning will remove
redundant neurons and filters in the model and result in thinner networks. In
this paper, we propose a gradually global pruning scheme for neuron level
pruning. In each pruning step, a small percent of neurons were selected and
dropped across all layers in the model. We also propose a simple method to
eliminate the biases in evaluating the importance of neurons to make the scheme
feasible. Compared with layer-wise pruning scheme, our scheme avoid the
difficulty in determining the redundancy in each layer and is more effective
for deep networks. Our scheme would automatically find a thinner sub-network in
original network under a given performance.
