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Proceedings Paper

Quantized wavelet scattering networks for signal classification
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Paper 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.

Paper Details

Date Published: 3 May 2019
PDF: 10 pages
Proc. SPIE 11003, Radar Sensor Technology XXIII, 110030V (3 May 2019); doi: 10.1117/12.2519659
Show Author Affiliations
Maxine R. Fox, The Pennsylvania State Univ. (United States)
Raghu G. Raj, U.S. Naval Research Lab. (United States)
Ram M. Narayanan, The Pennsylvania State Univ. (United States)

Published in SPIE Proceedings Vol. 11003:
Radar Sensor Technology XXIII
Kenneth I. Ranney; Armin Doerry, Editor(s)

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