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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 noveldoi:10.24963/ijcai.2020/359 dblp:conf/ijcai/LuoZLZWXFS20 fatcat:2hj4lgkc2fbwtisdoq5wiscy6q