  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.
