  Neuron pruning is an efficient method to compress the network into a slimmer
one for reducing the computational cost and storage overhead. Most of
state-of-the-art results are obtained in a layer-by-layer optimization mode. It
discards the unimportant input neurons and uses the survived ones to
reconstruct the output neurons approaching to the original ones in a
layer-by-layer manner. However, an unnoticed problem arises that the
information loss is accumulated as layer increases since the survived neurons
still do not encode the entire information as before. A better alternative is
to propagate the entire useful information to reconstruct the pruned layer
instead of directly discarding the less important neurons. To this end, we
propose a novel Layer Decomposition-Recomposition Framework (LDRF) for neuron
pruning, by which each layer's output information is recovered in an embedding
space and then propagated to reconstruct the following pruned layers with
useful information preserved. We mainly conduct our experiments on ILSVRC-12
benchmark with VGG-16 and ResNet-50. What should be emphasized is that our
results before end-to-end fine-tuning are significantly superior owing to the
information-preserving property of our proposed framework.With end-to-end
fine-tuning, we achieve state-of-the-art results of 5.13x and 3x speed-up with
only 0.5% and 0.65% top-5 accuracy drop respectively, which outperform the
existing neuron pruning methods.
