Matching Convolutional Neural Networks without Priors about Data

Carlos Eduardo Rosar Kos Lassance; Jean-Charles Vialatte; Vincent Gripon

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arXiv · 1802.09802

Matching Convolutional Neural Networks without Priors about Data

Carlos Eduardo Rosar Kos Lassance·Jean-Charles Vialatte·Vincent Gripon

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Abstract

We propose an extension of Convolutional Neural Networks (CNNs) to graph-structured data, including strided convolutions and data augmentation on graphs. Our method matches the accuracy of state-of-the-art CNNs when applied on images, without any prior about their 2D regular structure. On fMRI data, we obtain a significant gain in accuracy compared with existing graph-based alternatives.

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Carlos Eduardo Rosar Kos Lassance, Jean-Charles Vialatte, Vincent Gripon. 2018-02-27. Matching Convolutional Neural Networks without Priors about Data. https://arxiv.org/abs/1802.09802

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