CNN architectures for GRAPH data

Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro

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Abstract

In this ongoing work, we describe several architectures that generalize convolutional neural networks (CNNs) to process signals supported on graphs. The general idea of the replace time invariant filters with graph filters to generate convolutional features and to replace pooling with sampling schemes for graph signals. The different architectures are compared and the key trade offs are identified. Numerical simulations with both synthetic and real-world data are used to illustrate the advantages of the proposed approaches.

Original languageEnglish
Title of host publication2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Subtitle of host publicationProceedings
Place of PublicationPiscataway
PublisherIEEE
Pages723-724
Number of pages2
ISBN (Electronic)978-1-7281-1295-4
ISBN (Print) 978-1-7281-1296-1
DOIs
Publication statusPublished - 2019
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: 26 Nov 201829 Nov 2018

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Country/TerritoryUnited States
CityAnaheim
Period26/11/1829/11/18

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Convolutional neural networks
  • Deep learning
  • Geometric learning
  • Graph signal processing

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