One-Shot Multi-Rate Pruning of Graph Convolutional Networks
Hichem Sahbi
In this paper, we devise a novel lightweight Graph Convolutional Network
(GCN) design dubbed as Multi-Rate Magnitude Pruning (MRMP) that jointly trains
network topology and weights. Our method is variational and proceeds by
aligning the weight distribution of the learned networks with an a priori
distribution. In the one hand, this allows implementing any fixed pruning rate,
and also enhancing the generalization performances of the designed lightweight
GCNs. In the other hand, MRMP achieves a joint training of multiple GCNs, on
top of shared weights, in order to extrapolate accurate networks at any
targeted pruning rate without retraining their weights. Extensive experiments
conducted on the challenging task of skeleton-based recognition show a
substantial gain of our lightweight GCNs particularly at very high pruning
regimes.