  The ability of Deep Neural Networks to approximate highly complex functions
is key to their success. This benefit, however, comes at the expense of a large
model size, which challenges its deployment in resource-constrained
environments. Pruning is an effective technique used to limit this issue, but
often comes at the cost of reduced accuracy and adversarial robustness. This
paper addresses these shortcomings and introduces Deadwooding, a novel global
pruning technique that exploits a Lagrangian Dual method to encourage model
sparsity while retaining accuracy and ensuring robustness. The resulting model
is shown to significantly outperform the state-of-the-art studies in measures
of robustness and accuracy.
