Processing of incomplete images by (graph) convolutional neural networks

• URL: http://arxiv.org/abs/2010.13914v1
• Date: Mon, 26 Oct 2020 21:40:03 GMT
• Title: Processing of incomplete images by (graph) convolutional neural networks
• Authors: Tomasz Danel, Marek \'Smieja, {\L}ukasz Struski, Przemys{\l}aw Spurek, {\L}ukasz Maziarka
• Abstract summary: We investigate the problem of training neural networks from incomplete images without replacing missing values. We first represent an image as a graph, in which missing pixels are entirely ignored. The graph image representation is processed using a spatial graph convolutional network.
• Score: 7.778461949427663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the problem of training neural networks from incomplete images without replacing missing values. For this purpose, we first represent an image as a graph, in which missing pixels are entirely ignored. The graph image representation is processed using a spatial graph convolutional network (SGCN) -- a type of graph convolutional networks, which is a proper generalization of classical CNNs operating on images. On one hand, our approach avoids the problem of missing data imputation while, on the other hand, there is a natural correspondence between CNNs and SGCN. Experiments confirm that our approach performs better than analogical CNNs with the imputation of missing values on typical classification and reconstruction tasks.

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