论文标题
使用分离过滤器体系结构对卷积神经网络的计算优化
Computational optimization of convolutional neural networks using separated filters architecture
论文作者
论文摘要
本文考虑了卷积神经网络变换，可降低计算复杂性，从而加快神经网络处理。卷积神经网络（CNN）的用法是图像识别的标准方法，尽管事实上它们可能过于计算，例如在移动平台或嵌入式系统中识别。在本文中，我们提出了CNN结构转换，该结构转换表示2D卷积过滤器作为可分离过滤器的线性组合。它允许通过标准培训算法获得分离的卷积过滤器。我们研究了这种结构转换的计算效率，并建议通过CPU或GPU轻松处理快速实施。我们证明，专为字母和数字识别拟议结构的数字识别的CNN显示15％的加速，而工业图像识别系统的准确性损失没有准确性损失。总之，我们讨论了可能准确性降低的问题以及提出的转换在不同识别问题上的应用。卷积神经网络，计算优化，可分离过滤器，降低复杂性。
This paper considers a convolutional neural network transformation that reduces computation complexity and thus speedups neural network processing. Usage of convolutional neural networks (CNN) is the standard approach to image recognition despite the fact they can be too computationally demanding, for example for recognition on mobile platforms or in embedded systems. In this paper we propose CNN structure transformation which expresses 2D convolution filters as a linear combination of separable filters. It allows to obtain separated convolutional filters by standard training algorithms. We study the computation efficiency of this structure transformation and suggest fast implementation easily handled by CPU or GPU. We demonstrate that CNNs designed for letter and digit recognition of proposed structure show 15% speedup without accuracy loss in industrial image recognition system. In conclusion, we discuss the question of possible accuracy decrease and the application of proposed transformation to different recognition problems. convolutional neural networks, computational optimization, separable filters, complexity reduction.
