Row, column, and Cartesian product graphs built from lagged pixel correlations improve GNN image classification accuracy over grid and superpixel graphs on MNIST and Fashion-MNIST, with one exception.
Both datasets have 70,000 images with 60,000 im- ages for training and 10,000 for testing
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Inferring the Graph Structure of Images for Graph Neural Networks
Row, column, and Cartesian product graphs built from lagged pixel correlations improve GNN image classification accuracy over grid and superpixel graphs on MNIST and Fashion-MNIST, with one exception.