Graph Models
PGC-DGCNN
Introduced by Tran et al. in On Filter Size in Graph Convolutional NetworksPGC-DGCNN provides a new definition of graph convolutional filter. It generalizes the most commonly adopted filter, adding an hyper-parameter controlling the distance of the considered neighborhood. The model extends graph convolutions, following an intuition derived from the well-known convolutional filters over multi-dimensional tensors. The methods involves a simple, efficient and effective way to introduce a hyper-parameter on graph convolutions that influences the filter size, i.e. its receptive field over the considered graph.
Description and image from: On Filter Size in Graph Convolutional Networks
Source: On Filter Size in Graph Convolutional Networks
Papers
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Usage Over Time
Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
