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Compactly supported frame wavelets and applications in convolutional neural networks
Author(s): Nikolaos Karantzas; Kazem Safari; Mozahid Haque; Saeed Sarmadi; Manos Papadakis
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Paper Abstract

In this paper, we use the ideas presented in [1] to construct application-targeted convolutional neural network architectures (CNN). Specifically, we design frame filter banks consisting of sparse kernels with custom-selected orientations that can act as finite-difference operators. We then use these filter banks as the building blocks of structured receptive field CNNs [2] to compare baseline models with more application-oriented methods. Our tests are done on Google's Quick, Draw! data set.

Paper Details

Date Published: 9 September 2019
PDF: 13 pages
Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111380G (9 September 2019); doi: 10.1117/12.2530342
Show Author Affiliations
Nikolaos Karantzas, Univ. of Houston (United States)
Kazem Safari, Univ. of Houston (United States)
Mozahid Haque, Univ. of Houston (United States)
Saeed Sarmadi, Univ. of Houston (United States)
Manos Papadakis, Univ. of Houston (United States)


Published in SPIE Proceedings Vol. 11138:
Wavelets and Sparsity XVIII
Dimitri Van De Ville; Manos Papadakis; Yue M. Lu, Editor(s)

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