  Graph Convolutional Networks (GCNs) are widely used in a variety of
applications, and can be seen as an unstructured version of standard
Convolutional Neural Networks (CNNs). As in CNNs, the computational cost of
GCNs for large input graphs (such as large point clouds or meshes) can be high
and inhibit the use of these networks, especially in environments with low
computational resources. To ease these costs, quantization can be applied to
GCNs. However, aggressive quantization of the feature maps can lead to a
significant degradation in performance. On a different note, Haar wavelet
transforms are known to be one of the most effective and efficient approaches
to compress signals. Therefore, instead of applying aggressive quantization to
feature maps, we propose to utilize Haar wavelet compression and light
quantization to reduce the computations and the bandwidth involved with the
network. We demonstrate that this approach surpasses aggressive feature
quantization by a significant margin, for a variety of problems ranging from
node classification to point cloud classification and part and semantic
segmentation.
