Building compact convolutional neural networks (CNNs) with reliable
performance is a critical but challenging task, especially when deploying them
in real-world applications. As a common approach to reduce the size of CNNs,
pruning methods delete part of the CNN filters according to some metrics such
as $l1$-norm. However, previous methods hardly leverage the information
variance in a single feature map and the similarity characteristics among
feature maps. In this paper, we propose a novel filter pruning method, which
incorporates two kinds of feature map selections: diversity-aware selection
(DFS) and similarity-aware selection (SFS). DFS aims to discover features with
low information diversity while SFS removes features that have high
similarities with others. We conduct extensive empirical experiments with
various CNN architectures on publicly available datasets. The experimental
results demonstrate that our model obtains up to 91.6% parameter decrease and
83.7% FLOPs reduction with almost no accuracy loss.

本文提出一种新的滤波器剪枝方法，该方法结合了多个特征图选择机制：多样性感知选择和相似性感知选择，能够有效地减小卷积神经网络的参数大小和浮点运算数，同时几乎不会降低其分类准确度。