LcwtNet: Lifting complex wavelet layers for constructing a compact DNN model

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a new compact deep neural network (DNN) architecture based on lifting complex wavelets is proposed. The proposed DNN architecture (LcwtNet) is composed of multiple layers in addition to a CNN architecture. Complex wavelet and lifting wavelet layers are introduced as the lower layers of LcwtNet, which can reduce the number of parameters while maintaining high performance similar to that of CNN models. In simulations, the effectiveness of LcwtNet is demonstrated by several test results using the MNIST dataset.

Original languageEnglish
Title of host publication2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages288-293
Number of pages6
ISBN (Electronic)9781538646625
DOIs
Publication statusPublished - Jun 18 2018
Event17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 - Bilbao, Spain
Duration: Dec 18 2017Dec 20 2017

Publication series

Name2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017

Other

Other17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
Country/TerritorySpain
CityBilbao
Period12/18/1712/20/17

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Energy Engineering and Power Technology
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Signal Processing

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