Neural Behavior-Based Approach for Neural Network Pruning

Koji KAMMA, Yuki ISODA, Sarimu INOUE, Toshikazu WADA
<span title="2020-05-01">2020</span> <i title="Institute of Electronics, Information and Communications Engineers (IEICE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xosmgvetnbf4zpplikelekmdqe" style="color: black;">IEICE transactions on information and systems</a> </i> &nbsp;
This paper presents a method for reducing the redundancy in both fully connected layers and convolutional layers of trained neural network models. The proposed method consists of two steps, 1) Neuro-Coding: to encode the behavior of each neuron by a vector composed of its outputs corresponding to actual inputs and 2) Neuro-Unification: to unify the neurons having the similar behavioral vectors. Instead of just pruning one of the similar neurons, the proposed method let the remaining neuron
more &raquo; ... te the behavior of the pruned one. Therefore, the proposed method can reduce the number of neurons with small sacrifice of accuracy without retraining. Our method can be applied for compressing convolutional layers as well. In the convolutional layers, the behavior of each channel is encoded by its output feature maps, and channels whose behaviors can be well emulated by other channels are pruned and update the remaining weights. Through several experiments, we comfirmed that the proposed method performs better than the existing methods.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1587/transinf.2019edp7177">doi:10.1587/transinf.2019edp7177</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uawh24gzobhenhspsezyrg3qxm">fatcat:uawh24gzobhenhspsezyrg3qxm</a> </span>
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