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Deep network pruning is an effective method to reduce the storage andcomputation cost of deep neural networks when applying them to resource-limiteddevices. Among many pruning granularities, neuron level pruning will removeredundant neurons and filters in the model and result in thinner networks. Inthis paper, we propose a gradually global pruning scheme for neuron levelpruning. In each pruning step, a small percent of neurons were selected anddropped across all layers in the model. We also propose a simple method toeliminate the biases in evaluating the importance of neurons to make the schemefeasible. Compared with layer-wise pruning scheme, our scheme avoid thedifficulty in determining the redundancy in each layer and is more effectivefor deep networks. Our scheme would automatically find a thinner sub-network inoriginal network under a given performance.

Towards thinner convolutional neural networks through Gradually Global Pruning