一种新的轻量级深度卷积神经网络架构

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS
Baicheng Liu, Xi’ai Chen, Zhi Han, Huidi Jia, Yandong Tang
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引用次数: 0

摘要

深度卷积神经网络在许多计算机视觉任务中取得了很大的成功。然而,在某些存储空间有限的情况下,网络有数百万个参数限制了它的推理速度和使用。验证了基于低秩的方法和剪枝方法在压缩参数数量和提高深度卷积神经网络推理速度方面的有效性。随着价格的下降,网络的性能也随之下降。为了克服这一问题,本文设计了一种新颖的低秩稀疏卷积神经网络结构。除了加速推理速度和减少参数外,我们的方法取得了比基线网络更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Lightweight Architecture of Deep Convolutional Neural Networks
Deep convolutional neural networks have achieved much success in many computer vision tasks. However, a network has millions of parameters which limit its inference speed and usage for some situations with limited storage space. Low-rank based methods and pruning methods are verified effective to compress the number of parameters and accelerate inference speed of deep convolutional neural networks. As the price, the performance of the networks decreases. To overcome this problem, in this paper, we design a novel low-rank and sparse architecture of convolutional neural networks. Besides accelerating inference speed and reducing parameters, our approach achieves better performance than baseline networks.
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来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
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