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Unifying Local and Non-local Signal Processing with Graph CNNs

Gilles Puy, Srđan Kitić and Patrick Pérez

Abstract
This paper deals with the unification of local and non-local signal processing on graphs within a single convolutional neural network (CNN) framework. Building upon recent works on graph CNNs, we propose to use convolutional layers that take as inputs two variables, a signal and a graph, allowing the network to adapt to changes in the graph structure. In this article, we explain how this framework allows us to design a novel method to perform style transfer.

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DOI
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Bibtex
@inproceedings{dlid2017_2,

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