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首页> 外文会议>International conference on communications, signal processing, and systems >A Learning Automata-Based Compression Scheme for Convolutional Neural Network
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A Learning Automata-Based Compression Scheme for Convolutional Neural Network

机译：基于学习自动机的卷积神经网络压缩方案

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摘要

The convolutional neural network has been proved to be the state-of-the-art technique in image classification problems. In general, the improved recognition accuracy of the CNN is often accompanied by the increase of structure complexity. However, apart from the accuracy issues, computational resources and operating speed need to be considered on some occasions. Therefore, we propose an efficient compression scheme based on learning automata, which are usually used to choose the optimal action as a reinforcement learning method in this paper. Our proposed method can help the trained CNN to delete insignificant convolution kernels according to the actual requirements. According to the results of experiments, the proposed scheduling method can effectively compress the number of convolutional kernels at the expense of losing weak classification accuracy.
机译：卷积神经网络已被证明是图像分类问题中的最新技术。通常，CNN的识别精度提高通常伴随着结构复杂性的增加。但是，除了精度问题外，在某些情况下还需要考虑计算资源和操作速度。因此，本文提出了一种基于学习自动机的有效压缩方案，该方案通常用于选择最优动作作为强化学习方法。我们提出的方法可以帮助训练有素的CNN根据实际需求删除微不足道的卷积核。根据实验结果，提出的调度方法可以有效地压缩卷积核的数量，但会损失较弱的分类精度。

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