Title: Towards thinner convolutional neural networks through Gradually Global Pruning,
Abstract: 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.