A Study on Development of Wavelet Deep Learning

Zhong Zhang, Tatsuya Sugino, Takuma Akiduki, Tomoaki Mashimo

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

Abstract

In recent years, deep learning that can learn features from a dataset has been remarkably developing in the field of face recognition and voice recognition and so on. However, it is difficult to pursue cause of misjudgment result because input-output relation of deep learning is a black box. Furthermore, the content has yet to be elucidated what the judgment is based on. Therefore, when introducing deep Learning into multiple fields, it is important to understand the reason. This study aims to pursue cause of misjudgment result by intervening in the preprocessing part of deep learning using 2-dimensional discrete wavelet packet transform.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728129969
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event16th International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2019 - Kobe, Japan
Duration: Jul 7 2019Jul 10 2019

Publication series

NameInternational Conference on Wavelet Analysis and Pattern Recognition
Volume2019-July
ISSN (Print)2158-5695
ISSN (Electronic)2158-5709

Conference

Conference16th International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2019
Country/TerritoryJapan
CityKobe
Period7/7/197/10/19

Keywords

  • Convolutional neural network
  • Deep learning
  • Recognition
  • Signal processing
  • Wavelet transform

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition

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