Neural network compression empowers the effective yet unwieldy deep
convolutional neural networks (CNN) to be deployed in resource-constrained
scenarios. Most state-of-the-art approaches prune the model in filter-level
according to the "importance" of filters. Despite their success, we notice they
suffer from at least two of the following problems: 1) The redundancy among
filters is not considered because the importance is evaluated independently. 2)
Cross-layer filter comparison is unachievable since the importance is defined
locally within each layer. Consequently, we must manually specify layer-wise
pruning ratios. 3) They are prone to generate sub-optimal solutions because
they neglect the inequality between reducing parameters and reducing
computational cost. Reducing the same number of parameters in different
positions in the network may reduce different computational cost. To address
the above problems, we develop a novel algorithm named as COP
(correlation-based pruning), which can detect the redundant filters
efficiently. We enable the cross-layer filter comparison through global
normalization. We add parameter-quantity and computational-cost regularization
terms to the importance, which enables the users to customize the compression
according to their preference (smaller or faster). Extensive experiments have
shown COP outperforms the others significantly. The code is released at
this https URL

该研究提出了一种名为 COP（基于相关性的修剪）的算法来解决神经网络压缩中滤波器无法识别的问题，该算法可以检测冗余过滤器，通过全局标准化使跨层过滤器比较成为可能，并添加参数数量和计算成本规则化条款以便根据用户偏好（更小或者更快）来自定义压缩。

COP：基于正则化相关滤波器级别修剪的定制化深度模型压缩

COP: Customized Deep Model Compression via Regularized Correlation-Based  Filter-Level Pruning

While convolutional neural networks (CNN) have achieved impressive
performance on various classification/recognition tasks, they typically consist
of a massive number of parameters. This results in significant memory
requirement as well as computational overheads. Consequently, there is a
growing need for filter-level pruning approaches for compressing CNN based
models that not only reduce the total number of parameters but reduce the
overall computation as well. We present a new min-max framework for
filter-level pruning of CNNs. Our framework, called Play and Prune (PP),
jointly prunes and fine-tunes CNN model parameters, with an adaptive pruning
rate, while maintaining the model's predictive performance. Our framework
consists of two modules: (1) An adaptive filter pruning (AFP) module, which
minimizes the number of filters in the model; and (2) A pruning rate controller
(PRC) module, which maximizes the accuracy during pruning. Moreover, unlike
most previous approaches, our approach allows directly specifying the desired
error tolerance instead of pruning level. Our compressed models can be deployed
at run-time, without requiring any special libraries or hardware. Our approach
reduces the number of parameters of VGG-16 by an impressive factor of 17.5X,
and number of FLOPS by 6.43X, with no loss of accuracy, significantly
outperforming other state-of-the-art filter pruning methods.

本文提出了基于最小 - 最大框架的卷积神经网络滤波器级别剪枝方法，该方法同时对 CNN 模型参数进行剪枝和微调，具有自适应剪枝率和可直接指定误差容限等特点，在不损失准确性的情况下，有效减少了 VGG-16 的参数数量和 FLOPS。