A Method for Reducing Number of Parameters of Octave Convolution in Convolutional Neural Networks

Yusuke Gotoh, Yu Inoue

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Computational machine learning has attracted a great deal of attention for its ability to analyze large-scale data. In particular, convolutional neural networks (CNNs) have been proposed in the fields of image recognition and object detection in efforts to develop models with improved accuracy as well as more lightweight models that require a smaller number of parameters and a lower computational cost. Octave Convolution (OctConv) is a method used to reduce the memory and computational cost of a model while also improving its accuracy by replacing the conventional convolutional layer with an OctConv layer. However, the number of parameters used in OctConv is almost the same as that in the case of conventional convolutional processing. In this paper, we propose the Pointwise Octave Convolution (Pointwise OctConv) method, which combines the Pointwise Convolution (Pointwise Conv) method with OctConv to reduce the number of parameters used in OctConv and thus create a lighter model. In the proposed method, the number of parameters is reduced by performing Pointwise Conv before and after the convolution process for each path in the OctConv layer. In an evaluation using ResNet-56, the proposed method reduces the number of parameters by about 63.8 % with a loss of classification accuracy of 3.04 % when α= 0.75.

Original languageEnglish
Title of host publicationLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Pages212-222
Number of pages11
DOIs
Publication statusPublished - 2022

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
Volume118
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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