  Graph convolutional networks (GCNs) are nowadays becoming mainstream in
solving many image processing tasks including skeleton-based recognition. Their
general recipe consists in learning convolutional and attention layers that
maximize classification performances. With multi-head attention, GCNs are
highly accurate but oversized, and their deployment on edge devices requires
their pruning. Among existing methods, magnitude pruning (MP) is relatively
effective but its design is clearly suboptimal as network topology selection
and weight retraining are achieved independently. In this paper, we devise a
novel lightweight GCN design dubbed as Probabilistic Magnitude Pruning (PMP)
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. This allows implementing any fixed pruning rate, and also
enhancing the generalization performances of the designed lightweight GCNs.
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.
