CCPrune: Collaborative channel pruning for learning compact convolutional networks
Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Yanming [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:451 ; year:2021 ; day:3 ; month:09 ; pages:35-45 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.neucom.2021.04.063 |
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ELV054328179 |
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| 520 | |a Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. | ||
| 520 | |a Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. | ||
| 650 | 7 | |a Channel pruning |2 Elsevier | |
| 650 | 7 | |a Deep convolutional neural network |2 Elsevier | |
| 650 | 7 | |a Model compression |2 Elsevier | |
| 700 | 1 | |a Wen, Xiang |4 oth | |
| 700 | 1 | |a Zhang, Yiwen |4 oth | |
| 700 | 1 | |a Shi, Weisong |4 oth | |
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10.1016/j.neucom.2021.04.063 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001417.pica (DE-627)ELV054328179 (ELSEVIER)S0925-2312(21)00605-6 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Chen, Yanming verfasserin aut CCPrune: Collaborative channel pruning for learning compact convolutional networks 2021transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. Channel pruning Elsevier Deep convolutional neural network Elsevier Model compression Elsevier Wen, Xiang oth Zhang, Yiwen oth Shi, Weisong oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:451 year:2021 day:3 month:09 pages:35-45 extent:11 https://doi.org/10.1016/j.neucom.2021.04.063 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 451 2021 3 0903 35-45 11 |
| spelling |
10.1016/j.neucom.2021.04.063 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001417.pica (DE-627)ELV054328179 (ELSEVIER)S0925-2312(21)00605-6 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Chen, Yanming verfasserin aut CCPrune: Collaborative channel pruning for learning compact convolutional networks 2021transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. Channel pruning Elsevier Deep convolutional neural network Elsevier Model compression Elsevier Wen, Xiang oth Zhang, Yiwen oth Shi, Weisong oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:451 year:2021 day:3 month:09 pages:35-45 extent:11 https://doi.org/10.1016/j.neucom.2021.04.063 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 451 2021 3 0903 35-45 11 |
| allfields_unstemmed |
10.1016/j.neucom.2021.04.063 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001417.pica (DE-627)ELV054328179 (ELSEVIER)S0925-2312(21)00605-6 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Chen, Yanming verfasserin aut CCPrune: Collaborative channel pruning for learning compact convolutional networks 2021transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. Channel pruning Elsevier Deep convolutional neural network Elsevier Model compression Elsevier Wen, Xiang oth Zhang, Yiwen oth Shi, Weisong oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:451 year:2021 day:3 month:09 pages:35-45 extent:11 https://doi.org/10.1016/j.neucom.2021.04.063 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 451 2021 3 0903 35-45 11 |
| allfieldsGer |
10.1016/j.neucom.2021.04.063 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001417.pica (DE-627)ELV054328179 (ELSEVIER)S0925-2312(21)00605-6 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Chen, Yanming verfasserin aut CCPrune: Collaborative channel pruning for learning compact convolutional networks 2021transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. Channel pruning Elsevier Deep convolutional neural network Elsevier Model compression Elsevier Wen, Xiang oth Zhang, Yiwen oth Shi, Weisong oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:451 year:2021 day:3 month:09 pages:35-45 extent:11 https://doi.org/10.1016/j.neucom.2021.04.063 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 451 2021 3 0903 35-45 11 |
| allfieldsSound |
10.1016/j.neucom.2021.04.063 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001417.pica (DE-627)ELV054328179 (ELSEVIER)S0925-2312(21)00605-6 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Chen, Yanming verfasserin aut CCPrune: Collaborative channel pruning for learning compact convolutional networks 2021transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. Channel pruning Elsevier Deep convolutional neural network Elsevier Model compression Elsevier Wen, Xiang oth Zhang, Yiwen oth Shi, Weisong oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:451 year:2021 day:3 month:09 pages:35-45 extent:11 https://doi.org/10.1016/j.neucom.2021.04.063 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 451 2021 3 0903 35-45 11 |
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Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. |
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Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. |
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Deep convolutional neural networks (CNNs) is difficult to deploy on resource-constrained devices due to its huge amount of computation. Channel pruning is an effective method to reduce the amount of computation and accelerate network inference. Most of channels pruning methods use statistics from a single structure (convolutional layer or batch normalization layer) of the sparse network to evaluate the importance of channels. The limitation of these methods is that it may often mistakenly delete the important channels. In view of this, we propose a novel method, namely Collaborative Channel Pruning (CCPrune), to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors. The proposed method first introduces the regularization on the convolution layer weights and the BN layer scaling factors respectively. Then combine the weight of the convolutional layer and the scaling factor of the BN layer to evaluate the importance of the channel. Finally, it can delete the unimportant channels without reduces the performance of the model. The experimental results well demonstrate the effectiveness of our method. On CIFAR-10, it can reduce the FLOPs of VGG-19 by 85.50% while only slightly reducing the accuracy of the model, and it can reduce the FLOPs of Resnet-50 by 78.31% without reducing the accuracy of the model, respectively. |
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