Shallow convolutional neural network for image classification
Abstract Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly con...
Ausführliche Beschreibung
Autor*in: |
Lei, Fangyuan [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019 |
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Schlagwörter: |
Deep convolutional neural networks |
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Anmerkung: |
© Springer Nature Switzerland AG 2019 |
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Übergeordnetes Werk: |
Enthalten in: SN applied sciences - [Cham] : Springer International Publishing, 2019, 2(2019), 1 vom: 17. Dez. |
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Übergeordnetes Werk: |
volume:2 ; year:2019 ; number:1 ; day:17 ; month:12 |
Links: |
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DOI / URN: |
10.1007/s42452-019-1903-4 |
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Katalog-ID: |
SPR038582031 |
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520 | |a Abstract Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST. | ||
650 | 4 | |a Deep convolutional neural networks |7 (dpeaa)DE-He213 | |
650 | 4 | |a Shallow convolutional neural network |7 (dpeaa)DE-He213 | |
650 | 4 | |a Batch normalization |7 (dpeaa)DE-He213 | |
650 | 4 | |a Image classification |7 (dpeaa)DE-He213 | |
700 | 1 | |a Liu, Xun |4 aut | |
700 | 1 | |a Dai, Qingyun |4 aut | |
700 | 1 | |a Ling, Bingo Wing-Kuen |4 aut | |
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10.1007/s42452-019-1903-4 doi (DE-627)SPR038582031 (SPR)s42452-019-1903-4-e DE-627 ger DE-627 rakwb eng Lei, Fangyuan verfasserin (orcid)0000-0002-2059-8818 aut Shallow convolutional neural network for image classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST. Deep convolutional neural networks (dpeaa)DE-He213 Shallow convolutional neural network (dpeaa)DE-He213 Batch normalization (dpeaa)DE-He213 Image classification (dpeaa)DE-He213 Liu, Xun aut Dai, Qingyun aut Ling, Bingo Wing-Kuen aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2019), 1 vom: 17. Dez. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2019 number:1 day:17 month:12 https://dx.doi.org/10.1007/s42452-019-1903-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2019 1 17 12 |
spelling |
10.1007/s42452-019-1903-4 doi (DE-627)SPR038582031 (SPR)s42452-019-1903-4-e DE-627 ger DE-627 rakwb eng Lei, Fangyuan verfasserin (orcid)0000-0002-2059-8818 aut Shallow convolutional neural network for image classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST. Deep convolutional neural networks (dpeaa)DE-He213 Shallow convolutional neural network (dpeaa)DE-He213 Batch normalization (dpeaa)DE-He213 Image classification (dpeaa)DE-He213 Liu, Xun aut Dai, Qingyun aut Ling, Bingo Wing-Kuen aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2019), 1 vom: 17. Dez. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2019 number:1 day:17 month:12 https://dx.doi.org/10.1007/s42452-019-1903-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2019 1 17 12 |
allfields_unstemmed |
10.1007/s42452-019-1903-4 doi (DE-627)SPR038582031 (SPR)s42452-019-1903-4-e DE-627 ger DE-627 rakwb eng Lei, Fangyuan verfasserin (orcid)0000-0002-2059-8818 aut Shallow convolutional neural network for image classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST. Deep convolutional neural networks (dpeaa)DE-He213 Shallow convolutional neural network (dpeaa)DE-He213 Batch normalization (dpeaa)DE-He213 Image classification (dpeaa)DE-He213 Liu, Xun aut Dai, Qingyun aut Ling, Bingo Wing-Kuen aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2019), 1 vom: 17. Dez. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2019 number:1 day:17 month:12 https://dx.doi.org/10.1007/s42452-019-1903-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2019 1 17 12 |
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10.1007/s42452-019-1903-4 doi (DE-627)SPR038582031 (SPR)s42452-019-1903-4-e DE-627 ger DE-627 rakwb eng Lei, Fangyuan verfasserin (orcid)0000-0002-2059-8818 aut Shallow convolutional neural network for image classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST. Deep convolutional neural networks (dpeaa)DE-He213 Shallow convolutional neural network (dpeaa)DE-He213 Batch normalization (dpeaa)DE-He213 Image classification (dpeaa)DE-He213 Liu, Xun aut Dai, Qingyun aut Ling, Bingo Wing-Kuen aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2019), 1 vom: 17. Dez. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2019 number:1 day:17 month:12 https://dx.doi.org/10.1007/s42452-019-1903-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2019 1 17 12 |
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10.1007/s42452-019-1903-4 doi (DE-627)SPR038582031 (SPR)s42452-019-1903-4-e DE-627 ger DE-627 rakwb eng Lei, Fangyuan verfasserin (orcid)0000-0002-2059-8818 aut Shallow convolutional neural network for image classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST. Deep convolutional neural networks (dpeaa)DE-He213 Shallow convolutional neural network (dpeaa)DE-He213 Batch normalization (dpeaa)DE-He213 Image classification (dpeaa)DE-He213 Liu, Xun aut Dai, Qingyun aut Ling, Bingo Wing-Kuen aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2019), 1 vom: 17. Dez. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2019 number:1 day:17 month:12 https://dx.doi.org/10.1007/s42452-019-1903-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2019 1 17 12 |
language |
English |
source |
Enthalten in SN applied sciences 2(2019), 1 vom: 17. Dez. volume:2 year:2019 number:1 day:17 month:12 |
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Enthalten in SN applied sciences 2(2019), 1 vom: 17. Dez. volume:2 year:2019 number:1 day:17 month:12 |
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topic_facet |
Deep convolutional neural networks Shallow convolutional neural network Batch normalization Image classification |
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SN applied sciences |
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Lei, Fangyuan @@aut@@ Liu, Xun @@aut@@ Dai, Qingyun @@aut@@ Ling, Bingo Wing-Kuen @@aut@@ |
publishDateDaySort_date |
2019-12-17T00:00:00Z |
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shallow convolutional neural network for image classification |
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Shallow convolutional neural network for image classification |
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Abstract Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST. © Springer Nature Switzerland AG 2019 |
abstractGer |
Abstract Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST. © Springer Nature Switzerland AG 2019 |
abstract_unstemmed |
Abstract Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST. © Springer Nature Switzerland AG 2019 |
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7.3998938 |