  The most common method for DNN pruning is hard thresholding of network
weights, followed by retraining to recover any lost accuracy. Recently
developed smart pruning algorithms use the DNN response over the training set
for a variety of cost functions to determine redundant network weights, leading
to less accuracy degradation and possibly less retraining time. For experiments
on the total pruning time (pruning time + retraining time) we show that hard
thresholding followed by retraining remains the most efficient way of reducing
the number of network parameters. However smart pruning algorithms still have
advantages when retraining is not possible. In this context we propose a novel
smart pruning algorithm based on difference of convex functions optimisation
and show that it is often orders of magnitude faster than competing approaches
while achieving the lowest classification accuracy degradation. Furthermore we
investigate theoretically the effect of hard thresholding on DNN accuracy. We
show that accuracy degradation increases with remaining network depth from the
pruned layer. We also discover a link between the latent dimensionality of the
training data manifold and network robustness to hard thresholding.
