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1
Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2956976
Abstract: Deep learning has made significant progress in many fields such as image identification, speech recognition and natural language processing, especially in the field of computer vision. The better performance of the neural network often built…
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Keywords:
channel pruning;
separable convolution;
pruning algorithm;
depth wise ... See more keywords
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2
Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2022.3232566
Abstract: In the constrained computing environments such as mobile device or satellite on-board system, various computational factors of hardware resource can restrict the processing of deep learning (DL) services. Recent DL models such as satellite image…
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Keywords:
layer wise;
layer;
channel pruning;
shot manner ... See more keywords
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1
Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2021.3093835
Abstract: Deep neural networks have achieved remarkable advancement in various intelligence tasks. However, the massive computation and storage consumption limit applications on resource-constrained devices. While channel pruning has been widely applied to compress models, it is…
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Keywords:
neural networks;
grained channel;
acceleration;
deep neural ... See more keywords
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Published in 2020 at "IEEE Transactions on Circuits and Systems I: Regular Papers"
DOI: 10.1109/tcsi.2019.2958937
Abstract: Acceleration and compression on deep Convolutional Neural Networks (CNNs) have become a critical problem to develop intelligence on resource-constrained devices. Previous channel pruning can be easily deployed and accelerated without specialized hardware and software. However,…
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Keywords:
channel pruning;
low rank;
rank approximated;
network ... See more keywords
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1
Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2022.3156588
Abstract: Deep convolutional neural networks (CNNs) have achieved tremendous successes but tend to suffer from high computation costs mainly due to heavy over-parameterization, resulting in the difficulty of directly applying them to the ever-growing application demands…
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Keywords:
layer wise;
sparsity;
task;
channel pruning ... See more keywords
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Published in 2021 at "IEEE Transactions on Emerging Topics in Computing"
DOI: 10.1109/tetc.2021.3050770
Abstract: Deep learning models have evolved into powerful tools that can be used for many artificial intelligence tasks. However, deploying deep neural networks into real-world applications is still challenging due to their high computational complexity and…
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Keywords:
channel pruning;
learning low;
quantization;
resource consumption ... See more keywords
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1
Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3165123
Abstract: Although neural networks have achieved great success in various fields, applications on mobile devices are limited by the computational and storage costs required for large models. The model compression (neural network pruning) technology can significantly…
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Keywords:
channel pruning;
network channel;
differentiable network;
model compression ... See more keywords
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2
Published in 2023 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.48550/arxiv.2303.11923
Abstract: Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance. Previous works focus on either single task model pruning or simply adapting…
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Keywords:
performance aware;
global channel;
task;
channel pruning ... See more keywords