  We propose a novel network pruning approach by information preserving of
pre-trained network weights (filters). Network pruning with the information
preserving is formulated as a matrix sketch problem, which is efficiently
solved by the off-the-shelf Frequent Direction method. Our approach, referred
to as FilterSketch, encodes the second-order information of pre-trained
weights, which enables the representation capacity of pruned networks to be
recovered with a simple fine-tuning procedure. FilterSketch requires neither
training from scratch nor data-driven iterative optimization, leading to a
several-orders-of-magnitude reduction of time cost in the optimization of
pruning. Experiments on CIFAR-10 show that FilterSketch reduces 63.3% of FLOPs
and prunes 59.9% of network parameters with negligible accuracy cost for
ResNet-110. On ILSVRC-2012, it reduces 45.5% of FLOPs and removes 43.0% of
parameters with only 0.69% accuracy drop for ResNet-50. Our code and pruned
models can be found at https://github.com/lmbxmu/FilterSketch.
