Deep Convolutional Neural Networks~(CNNs) offer remarkable performance of
classifications and regressions in many high-dimensional problems and have been
widely utilized in real-word cognitive applications. However, high
computational cost of CNNs greatly hinder their deployment in
resource-constrained applications, real-time systems and edge computing
platforms. To overcome this challenge, we propose a novel filter-pruning
framework, two-phase filter pruning based on conditional entropy, namely
\textit{2PFPCE}, to compress the CNN models and reduce the inference time with
marginal performance degradation. In our proposed method, we formulate filter
pruning process as an optimization problem and propose a novel filter selection
criteria measured by conditional entropy. Based on the assumption that the
representation of neurons shall be evenly distributed, we also develop a
maximum-entropy filter freeze technique that can reduce over fitting. Two
filter pruning strategies -- global and layer-wise strategies, are compared.
Our experiment result shows that combining these two strategies can achieve a
higher neural network compression ratio than applying only one of them under
the same accuracy drop threshold. Two-phase pruning, that is, combining both
global and layer-wise strategies, achieves 10 X FLOPs reduction and 46%
inference time reduction on VGG-16, with 2% accuracy drop.

本文提出了一种新的基于条件熵的两阶段滤波器修剪框架，即 2PFPCE，来压缩 CNN 模型并减少推理时间，实验结果表明，将整体和逐层两种滤波策略结合起来可以实现更高的神经网络压缩比，并且可以取得高达 10 倍的 FLOPs 降低和 46% 的推理时间降低率，在精度下降 2％的情况下。