  Dynamic model pruning is a recent direction that allows for the inference of
a different sub-network for each input sample during deployment. However,
current dynamic methods rely on learning a continuous channel gating through
regularization by inducing sparsity loss. This formulation introduces
complexity in balancing different losses (e.g task loss, regularization loss).
In addition, regularization based methods lack transparent tradeoff
hyperparameter selection to realize a computational budget. Our contribution is
two-fold: 1) decoupled task and pruning losses. 2) Simple hyperparameter
selection that enables FLOPs reduction estimation before training. Inspired by
the Hebbian theory in Neuroscience: "neurons that fire together wire together",
we propose to predict a mask to process k filters in a layer based on the
activation of its previous layer. We pose the problem as a self-supervised
binary classification problem. Each mask predictor module is trained to predict
if the log-likelihood for each filter in the current layer belongs to the top-k
activated filters. The value k is dynamically estimated for each input based on
a novel criterion using the mass of heatmaps. We show experiments on several
neural architectures, such as VGG, ResNet and MobileNet on CIFAR and ImageNet
datasets. On CIFAR, we reach similar accuracy to SOTA methods with 15% and 24%
higher FLOPs reduction. Similarly in ImageNet, we achieve lower drop in
accuracy with up to 13% improvement in FLOPs reduction.
