To reduce the significant redundancy in deep Convolutional Neural Networks
(CNNs), most existing methods prune neurons by only considering statistics of
an individual layer or two consecutive layers (e.g., prune one layer to
minimize the reconstruction error of the next layer), ignoring the effect of
error propagation in deep networks. In contrast, we argue that it is essential
to prune neurons in the entire neuron network jointly based on a unified goal:
minimizing the reconstruction error of important responses in the "final
response layer" (FRL), which is the second-to-last layer before classification,
for a pruned network to retrain its predictive power. Specifically, we apply
feature ranking techniques to measure the importance of each neuron in the FRL,
and formulate network pruning as a binary integer optimization problem and
derive a closed-form solution to it for pruning neurons in earlier layers.
Based on our theoretical analysis, we propose the Neuron Importance Score
Propagation (NISP) algorithm to propagate the importance scores of final
responses to every neuron in the network. The CNN is pruned by removing neurons
with least importance, and then fine-tuned to retain its predictive power. NISP
is evaluated on several datasets with multiple CNN models and demonstrated to
achieve significant acceleration and compression with negligible accuracy loss.

本论文提出了通过神经元网络联合剪枝最小化最终权值层的重建误差来提升 CNN 的计算效率与压缩率的方法，并推导了闭合形式的解决方案来解决神经元剪枝问题。结果表明，利用该方法可以实现显著的加速和压缩，且几乎不影响 CNN 的预测能力。