Quantized wavelet scattering networks for signal classification

Maxine R. Fox, Raghu G. Raj, Ram M. Narayanan

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

Abstract

While convolutional neural networks (CNNs) are powerful tools in machine learning, their construction is far from a science. In addition, instantiations of CNNs are highly memory expensive and typically require large training sets. Wavelet scattering networks (WSNs) could provide a simple means of testing quantization schemes for CNNs, without the added complexity of adjustable parameters. Using the MSTAR database, the performance of a WSN in combination with several quantization schemes is examined.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXIII
EditorsKenneth I. Ranney, Armin Doerry
PublisherSPIE
ISBN (Electronic)9781510626713
DOIs
StatePublished - Jan 1 2019
EventRadar Sensor Technology XXIII 2019 - Baltimore, United States
Duration: Apr 15 2019Apr 17 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11003
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceRadar Sensor Technology XXIII 2019
CountryUnited States
CityBaltimore
Period4/15/194/17/19

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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