MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning
Published: Oct 1, 2019
Abstract
In this paper, we propose a novel meta learning approach for automatic channel pruning of very deep neural networks. We first train a PruningNet, a kind of meta network, which is able to generate weight parameters for any pruned structure given the target network. We use a simple stochastic structure sampling method for training the PruningNet. Then, we apply an evolutionary procedure to search for good-performing pruned networks. The search is...
Paper Details
Title
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning
Published Date
Oct 1, 2019
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