Receding Neuron Importances for Structured Pruning

• URL: http://arxiv.org/abs/2204.06404v1
• Date: Wed, 13 Apr 2022 14:08:27 GMT
• Title: Receding Neuron Importances for Structured Pruning
• Authors: Mihai Suteu and Yike Guo
• Abstract summary: Structured pruning efficiently compresses networks by identifying and removing unimportant neurons. We introduce a simple BatchNorm variation with bounded scaling parameters, based on which we design a novel regularisation term that suppresses only neurons with low importance. We show that neural networks trained this way can be pruned to a larger extent and with less deterioration.
• Score: 11.375436522599133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structured pruning efficiently compresses networks by identifying and removing unimportant neurons. While this can be elegantly achieved by applying sparsity-inducing regularisation on BatchNorm parameters, an L1 penalty would shrink all scaling factors rather than just those of superfluous neurons. To tackle this issue, we introduce a simple BatchNorm variation with bounded scaling parameters, based on which we design a novel regularisation term that suppresses only neurons with low importance. Under our method, the weights of unnecessary neurons effectively recede, producing a polarised bimodal distribution of importances. We show that neural networks trained this way can be pruned to a larger extent and with less deterioration. We one-shot prune VGG and ResNet architectures at different ratios on CIFAR and ImagenNet datasets. In the case of VGG-style networks, our method significantly outperforms existing approaches particularly under a severe pruning regime.

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