Results 31 to 40 of about 14,609 (251)
Learning compact ConvNets through filter pruning based on the saliency of a feature map
IET Image Processing, 2022 With the performance increase of convolutional neural network (CNN), the disadvantages of CNN's high storage and high power consumption are followed. Among the methods mentioned in various literature, filter pruning is a crucial method for constructing ...
Zhoufeng Liu +4 more
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Neural Network Compression via Low Frequency Preference
Remote Sensing, 2023 Network pruning has been widely used in model compression techniques, and offers a promising prospect for deploying models on devices with limited resources.
Chaoyan Zhang +3 more
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Pruning of Network Filters for Small Dataset [PDF]
IEEE Access, 2020 Many existing object detection networks achieve outstanding accuracy in some open-source datasets, which usually contain a large number of images and target categories. However, a majority of the state-of-the-art networks use hundreds of filters and layers to extract plenty of features on behalf of getting a better result.
Zhuang Li, Lihong Xu, Shuwei Zhu
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Progressive Kernel Pruning Based on the Information Mapping Sparse Index for CNN Compression
IEEE Access, 2021 Network pruning can effectively reduce a model's capacity and computational load, thereby making model deployment in mobile devices less difficult than that without pruning.
Jihong Zhu, Yang Zhao, Jihong Pei
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Pruning Filters for Efficient ConvNets
CoRR, 2016 The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads involve pruning and compressing the weights of various layers without hurting original accuracy.
Hao Li 0022 +4 more
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Scene classification of remote sensing image based on compound pruning [PDF]
MATEC Web of Conferences, 2021 Convolution neural network for remote sensing image scene classification consumes a lot of time and storage space to train, test and save the model. In this paper, firstly, elastic variables are defined for convolution layer filter, and combined with ...
Jiang Fengbing, Li Fang, Yang Guoliang
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GHFP: Gradually Hard Filter Pruning
CoRR, 2020 Filter pruning is widely used to reduce the computation of deep learning, enabling the deployment of Deep Neural Networks (DNNs) in resource-limited devices. Conventional Hard Filter Pruning (HFP) method zeroizes pruned filters and stops updating them, thus reducing the search space of the model.
Linhang Cai, Zhulin An, Yongjun Xu 0001
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Pruning Convolutional Filters Using Batch Bridgeout [PDF]
IEEE Access, 2020 State-of-the-art computer vision models are rapidly increasing in capacity, where the number of parameters far exceeds the number required to fit the training set. This results in better optimization and generalization performance. However, the huge size of contemporary models results in large inference costs and limits their use on resource-limited ...
Najeeb Khan, Ian Stavness
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A Compact Parallel Pruning Scheme for Deep Learning Model and Its Mobile Instrument Deployment
Mathematics, 2022 In the single pruning algorithm, channel pruning or filter pruning is used to compress the deep convolution neural network, and there are still many redundant parameters in the compressed model.
Meng Li +4 more
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Pruning CNN’s with Linear Filter Ensembles
, 2020 accepted to ...
Csanád Sándor +2 more
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