A Framework For Pruning Deep Neural Networks Using Energy-Based Models

• URL: http://arxiv.org/abs/2102.13188v1
• Date: Thu, 25 Feb 2021 21:44:19 GMT
• Title: A Framework For Pruning Deep Neural Networks Using Energy-Based Models
• Authors: Hojjat Salehinejad, Shahrokh Valaee
• Abstract summary: A typical deep neural network (DNN) has a large number of trainable parameters. We propose a framework for pruning DNNs based on a population-based global optimization method. Experiments on ResNets, AlexNet, and SqueezeNet show a pruning rate of more than $50%$ of the trainable parameters.
• Score: 45.4796383952516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A typical deep neural network (DNN) has a large number of trainable parameters. Choosing a network with proper capacity is challenging and generally a larger network with excessive capacity is trained. Pruning is an established approach to reducing the number of parameters in a DNN. In this paper, we propose a framework for pruning DNNs based on a population-based global optimization method. This framework can use any pruning objective function. As a case study, we propose a simple but efficient objective function based on the concept of energy-based models. Our experiments on ResNets, AlexNet, and SqueezeNet for the CIFAR-10 and CIFAR-100 datasets show a pruning rate of more than $50\%$ of the trainable parameters with approximately $<5\%$ and $<1\%$ drop of Top-1 and Top-5 classification accuracy, respectively.

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