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Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations

2022-03-07 15:16:31
Naman Goyal, David Steiner
arXiv_CV
         
Abstract

In this paper, we will evaluate the performance of graph neural networks in two distinct domains: computer vision and reinforcement learning. In the computer vision section, we seek to learn whether a novel non-redundant representation for images as graphs can improve performance over trivial pixel to node mapping on a graph-level prediction graph, specifically image classification. For the reinforcement learning section, we seek to learn if explicitly modeling solving a Rubik's cube as a graph problem can improve performance over a standard model-free technique with no inductive bias.

Abstract (translated)

URL

https://arxiv.org/abs/2203.03457

PDF

https://arxiv.org/pdf/2203.03457.pdf

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