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Applying Graph Neural Networks for Better Image Classification
This article explains how to adapt Graph Neural Networks (GNNs) for image classification. It covers the process from converting images into graphs to updating the model’s parameters. I explore how message passing and global pooling can enhance spatia...
alexisvannson.hashnode.dev6 min read
#graph-neural-network#image-classification#gnn#gnns#machine-learning#machine-learning-from-scratch#machine-learning-algorithm#machine-learning-models#neural-networks#deep-learning#message-passing#how-to-use-graph-neural-networks-for-image-classification
klement Gunndu
Agentic AI Wizard
The global pooling step you describe is where a lot of the expressiveness gets compressed — one thing worth experimenting with is differentiable pooling (DiffPool) instead of mean/max global pooling when the graph structure varies significantly across images. The message passing improvement over CNN feature maps tends to show the most gain on images with irregular spatial structure, like satellite imagery or medical scans, where the grid assumption of convolutions is the weakest.