Voronoi Convolutional Neural Networks

• URL: http://arxiv.org/abs/2010.11339v1
• Date: Wed, 21 Oct 2020 22:42:19 GMT
• Title: Voronoi Convolutional Neural Networks
• Authors: Soroosh Yazdani and Andrea Tagliasacchi
• Abstract summary: We show that by treating the samples as the average of a function within a cell, we can find a natural equivalent of most layers used in CNN. We also present an algorithm for running inference for these models exactly using standard convex geometry algorithms.
• Score: 22.793216189458402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this technical report, we investigate extending convolutional neural networks to the setting where functions are not sampled in a grid pattern. We show that by treating the samples as the average of a function within a cell, we can find a natural equivalent of most layers used in CNN. We also present an algorithm for running inference for these models exactly using standard convex geometry algorithms.

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