Abstract：Deep neural networks (DNN) have many parameters, which restricts the use of DNN in scenarios with limited computing resources or when speed is a priority. Some researchers have proposed to prune the DNN using singular value decomposition (SVD). However, this method lacks adaptivity as it prunes the same number of singular values in all the hidden DNN layers. A singular rate pruning factor (SRPF) based DNN pruning method is given here. This method first separately calculates the SRPFs for each hidden layer based on the data with every layer then pruned using different pruning factors. This method makes full use of the distribution traits of the singular values in each hidden layer. This method is more adaptive than pruning a fixed portion of singular values with experiments showing that a DNN pruned with this method performs better. A retraining method is also given which adapts to the pruned DNN.
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