Journal Article10.1016/J.NEUCOM.2021.04.063
CCPrune: Collaborative channel pruning for learning compact convolutional networks
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TL;DR: Wang et al. as mentioned in this paper proposed a method called collaborative channel pruning (CCPrune) to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors.
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About: This article is published in Neurocomputing. The article was published on 03 Sep 2021. The article focuses on the topics: Pruning (decision trees) & Convolutional neural network.
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Citations
EACP: An effective automatic channel pruning for neural networks
TL;DR: Wang et al. as discussed by the authors adopt the k-means++ method to cluster filters with similar features hierarchically in each convolutional layer and use an improved social group optimization (SGO) algorithm to iteratively search and optimize the compression process of the post-clustered structure to find the optimal compressed structure.
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Convolutional neural network pruning based on multi-objective feature map selection for image classification
Pengcheng Jiang,Yu Xue,F. Neri +2 more
TL;DR: In this article , a multi-objective pruning based on feature map selection (MOP-FMS) is proposed, which uses the number of floating point operations (FLOPs) as a pruning objective in addition to the accuracy of the pruned network.
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FPC: Filter pruning via the contribution of output feature map for deep convolutional neural networks acceleration
TL;DR: In this article , the authors proposed a filter pruning method based on the contribution of the output feature map, which considers the diverse information carried by different output feature maps and then effectively delete low contribution part without reducing the model performance.
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FPFS: Filter-level pruning via distance weight measuring filter similarity
Wei Zhang,Zhiming Wang +1 more
TL;DR: In this article , a distance-based filter selection method, called FPFS, is proposed to visualize the similarity between filters from a global perspective and calculate and sum the distance between filters to get filters' distance weight, which is applied as a metric to assess filters.
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RUFP: Reinitializing unimportant filters for soft pruning
Ke Zhang,Guangzhe Liu,Meibo Lv +2 more
TL;DR: Zhang et al. as mentioned in this paper proposed a novel method, termed RUFP, to reinitialize unimportant filters according to the most important one, which not only gives these filters a chance to be reactivated, but also introduces more filter forms that may win the initialization lottery.
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