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

2 Scopus citations


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
ISBN (Electronic)9781510626713
StatePublished - 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
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceRadar Sensor Technology XXIII 2019
Country/TerritoryUnited States

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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