High-parameter-efficiency convolutional neural networks

Abstract Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper pro...
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Autor*in:	Lu, Yao [verfasserIn] Lu, Guangming [verfasserIn] Li, Jinxing [verfasserIn] Xu, Yuanrong [verfasserIn] Zhang, David [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Mobile CNNs Multiple group reused convolutions Decomposed point-wise convolutions High-parameter-efficiency convolutions

Übergeordnetes Werk:	Enthalten in: Neural computing & applications - London : Springer, 1993, 32(2019), 14 vom: 26. Nov., Seite 10633-10644
Übergeordnetes Werk:	volume:32 ; year:2019 ; number:14 ; day:26 ; month:11 ; pages:10633-10644

Links:	Volltext

DOI / URN:	10.1007/s00521-019-04596-w

Katalog-ID:	SPR040181316

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520			\|a Abstract Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper proposes two novel convolutional kernels, multiple group reused convolutions (MGRCs) and decomposed point-wise convolutions (DPCs), to improve the efficiency of parameters by removing the parameter’s redundancy. The summation of MGRC and DPC is called high-parameter-efficiency convolutions (HPEC) in this paper, and the relevant CNNs can be called HPE-CNNs. Experimental results showed that, compared with the traditional convolutional kernels, HPEC can greatly decrease the model size without affecting the performance. Additionally, since the HPE-CNNs reduce the redundancy of parameters more thoroughly than the other mobile CNNs, they can address the over-fitting problem more effectively on the challenging datasets with less abundant training information.
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matchkey_str	article:14333058:2019----::ihaaeeefceccnouinl
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allfields	10.1007/s00521-019-04596-w doi (DE-627)SPR040181316 (SPR)s00521-019-04596-w-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Lu, Yao verfasserin aut High-parameter-efficiency convolutional neural networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper proposes two novel convolutional kernels, multiple group reused convolutions (MGRCs) and decomposed point-wise convolutions (DPCs), to improve the efficiency of parameters by removing the parameter’s redundancy. The summation of MGRC and DPC is called high-parameter-efficiency convolutions (HPEC) in this paper, and the relevant CNNs can be called HPE-CNNs. Experimental results showed that, compared with the traditional convolutional kernels, HPEC can greatly decrease the model size without affecting the performance. Additionally, since the HPE-CNNs reduce the redundancy of parameters more thoroughly than the other mobile CNNs, they can address the over-fitting problem more effectively on the challenging datasets with less abundant training information. Mobile CNNs (dpeaa)DE-He213 Multiple group reused convolutions (dpeaa)DE-He213 Decomposed point-wise convolutions (dpeaa)DE-He213 High-parameter-efficiency convolutions (dpeaa)DE-He213 Lu, Guangming verfasserin aut Li, Jinxing verfasserin aut Xu, Yuanrong verfasserin aut Zhang, David verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2019), 14 vom: 26. Nov., Seite 10633-10644 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2019 number:14 day:26 month:11 pages:10633-10644 https://dx.doi.org/10.1007/s00521-019-04596-w 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 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_2006 GBV_ILN_2007 GBV_ILN_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 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_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 54.72 ASE AR 32 2019 14 26 11 10633-10644
spelling	10.1007/s00521-019-04596-w doi (DE-627)SPR040181316 (SPR)s00521-019-04596-w-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Lu, Yao verfasserin aut High-parameter-efficiency convolutional neural networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper proposes two novel convolutional kernels, multiple group reused convolutions (MGRCs) and decomposed point-wise convolutions (DPCs), to improve the efficiency of parameters by removing the parameter’s redundancy. The summation of MGRC and DPC is called high-parameter-efficiency convolutions (HPEC) in this paper, and the relevant CNNs can be called HPE-CNNs. Experimental results showed that, compared with the traditional convolutional kernels, HPEC can greatly decrease the model size without affecting the performance. Additionally, since the HPE-CNNs reduce the redundancy of parameters more thoroughly than the other mobile CNNs, they can address the over-fitting problem more effectively on the challenging datasets with less abundant training information. Mobile CNNs (dpeaa)DE-He213 Multiple group reused convolutions (dpeaa)DE-He213 Decomposed point-wise convolutions (dpeaa)DE-He213 High-parameter-efficiency convolutions (dpeaa)DE-He213 Lu, Guangming verfasserin aut Li, Jinxing verfasserin aut Xu, Yuanrong verfasserin aut Zhang, David verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2019), 14 vom: 26. Nov., Seite 10633-10644 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2019 number:14 day:26 month:11 pages:10633-10644 https://dx.doi.org/10.1007/s00521-019-04596-w 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 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_2006 GBV_ILN_2007 GBV_ILN_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 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_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 54.72 ASE AR 32 2019 14 26 11 10633-10644
allfields_unstemmed	10.1007/s00521-019-04596-w doi (DE-627)SPR040181316 (SPR)s00521-019-04596-w-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Lu, Yao verfasserin aut High-parameter-efficiency convolutional neural networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper proposes two novel convolutional kernels, multiple group reused convolutions (MGRCs) and decomposed point-wise convolutions (DPCs), to improve the efficiency of parameters by removing the parameter’s redundancy. The summation of MGRC and DPC is called high-parameter-efficiency convolutions (HPEC) in this paper, and the relevant CNNs can be called HPE-CNNs. Experimental results showed that, compared with the traditional convolutional kernels, HPEC can greatly decrease the model size without affecting the performance. Additionally, since the HPE-CNNs reduce the redundancy of parameters more thoroughly than the other mobile CNNs, they can address the over-fitting problem more effectively on the challenging datasets with less abundant training information. Mobile CNNs (dpeaa)DE-He213 Multiple group reused convolutions (dpeaa)DE-He213 Decomposed point-wise convolutions (dpeaa)DE-He213 High-parameter-efficiency convolutions (dpeaa)DE-He213 Lu, Guangming verfasserin aut Li, Jinxing verfasserin aut Xu, Yuanrong verfasserin aut Zhang, David verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2019), 14 vom: 26. Nov., Seite 10633-10644 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2019 number:14 day:26 month:11 pages:10633-10644 https://dx.doi.org/10.1007/s00521-019-04596-w 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 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_2006 GBV_ILN_2007 GBV_ILN_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 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_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 54.72 ASE AR 32 2019 14 26 11 10633-10644
allfieldsGer	10.1007/s00521-019-04596-w doi (DE-627)SPR040181316 (SPR)s00521-019-04596-w-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Lu, Yao verfasserin aut High-parameter-efficiency convolutional neural networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper proposes two novel convolutional kernels, multiple group reused convolutions (MGRCs) and decomposed point-wise convolutions (DPCs), to improve the efficiency of parameters by removing the parameter’s redundancy. The summation of MGRC and DPC is called high-parameter-efficiency convolutions (HPEC) in this paper, and the relevant CNNs can be called HPE-CNNs. Experimental results showed that, compared with the traditional convolutional kernels, HPEC can greatly decrease the model size without affecting the performance. Additionally, since the HPE-CNNs reduce the redundancy of parameters more thoroughly than the other mobile CNNs, they can address the over-fitting problem more effectively on the challenging datasets with less abundant training information. Mobile CNNs (dpeaa)DE-He213 Multiple group reused convolutions (dpeaa)DE-He213 Decomposed point-wise convolutions (dpeaa)DE-He213 High-parameter-efficiency convolutions (dpeaa)DE-He213 Lu, Guangming verfasserin aut Li, Jinxing verfasserin aut Xu, Yuanrong verfasserin aut Zhang, David verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2019), 14 vom: 26. Nov., Seite 10633-10644 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2019 number:14 day:26 month:11 pages:10633-10644 https://dx.doi.org/10.1007/s00521-019-04596-w 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 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_2006 GBV_ILN_2007 GBV_ILN_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 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_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 54.72 ASE AR 32 2019 14 26 11 10633-10644
allfieldsSound	10.1007/s00521-019-04596-w doi (DE-627)SPR040181316 (SPR)s00521-019-04596-w-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Lu, Yao verfasserin aut High-parameter-efficiency convolutional neural networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper proposes two novel convolutional kernels, multiple group reused convolutions (MGRCs) and decomposed point-wise convolutions (DPCs), to improve the efficiency of parameters by removing the parameter’s redundancy. The summation of MGRC and DPC is called high-parameter-efficiency convolutions (HPEC) in this paper, and the relevant CNNs can be called HPE-CNNs. Experimental results showed that, compared with the traditional convolutional kernels, HPEC can greatly decrease the model size without affecting the performance. Additionally, since the HPE-CNNs reduce the redundancy of parameters more thoroughly than the other mobile CNNs, they can address the over-fitting problem more effectively on the challenging datasets with less abundant training information. Mobile CNNs (dpeaa)DE-He213 Multiple group reused convolutions (dpeaa)DE-He213 Decomposed point-wise convolutions (dpeaa)DE-He213 High-parameter-efficiency convolutions (dpeaa)DE-He213 Lu, Guangming verfasserin aut Li, Jinxing verfasserin aut Xu, Yuanrong verfasserin aut Zhang, David verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2019), 14 vom: 26. Nov., Seite 10633-10644 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2019 number:14 day:26 month:11 pages:10633-10644 https://dx.doi.org/10.1007/s00521-019-04596-w 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 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_2006 GBV_ILN_2007 GBV_ILN_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 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_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 54.72 ASE AR 32 2019 14 26 11 10633-10644
language	English
source	Enthalten in Neural computing & applications 32(2019), 14 vom: 26. Nov., Seite 10633-10644 volume:32 year:2019 number:14 day:26 month:11 pages:10633-10644
sourceStr	Enthalten in Neural computing & applications 32(2019), 14 vom: 26. Nov., Seite 10633-10644 volume:32 year:2019 number:14 day:26 month:11 pages:10633-10644
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Mobile CNNs Multiple group reused convolutions Decomposed point-wise convolutions High-parameter-efficiency convolutions
dewey-raw	004
isfreeaccess_bool	false
container_title	Neural computing & applications
authorswithroles_txt_mv	Lu, Yao @@aut@@ Lu, Guangming @@aut@@ Li, Jinxing @@aut@@ Xu, Yuanrong @@aut@@ Zhang, David @@aut@@
publishDateDaySort_date	2019-11-26T00:00:00Z
hierarchy_top_id	271595574
dewey-sort	14
id	SPR040181316
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR040181316</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110191250.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-019-04596-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR040181316</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00521-019-04596-w-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lu, Yao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">High-parameter-efficiency convolutional neural networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper proposes two novel convolutional kernels, multiple group reused convolutions (MGRCs) and decomposed point-wise convolutions (DPCs), to improve the efficiency of parameters by removing the parameter’s redundancy. The summation of MGRC and DPC is called high-parameter-efficiency convolutions (HPEC) in this paper, and the relevant CNNs can be called HPE-CNNs. Experimental results showed that, compared with the traditional convolutional kernels, HPEC can greatly decrease the model size without affecting the performance. Additionally, since the HPE-CNNs reduce the redundancy of parameters more thoroughly than the other mobile CNNs, they can address the over-fitting problem more effectively on the challenging datasets with less abundant training information.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mobile CNNs</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiple group reused convolutions</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decomposed point-wise convolutions</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">High-parameter-efficiency convolutions</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lu, Guangming</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jinxing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Yuanrong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, David</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">London : Springer, 1993</subfield><subfield code="g">32(2019), 14 vom: 26. Nov., Seite 10633-10644</subfield><subfield code="w">(DE-627)271595574</subfield><subfield code="w">(DE-600)1480526-1</subfield><subfield code="x">1433-3058</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:32</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:14</subfield><subfield code="g">day:26</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:10633-10644</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00521-019-04596-w</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield 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author	Lu, Yao
spellingShingle	Lu, Yao ddc 004 bkl 54.72 misc Mobile CNNs misc Multiple group reused convolutions misc Decomposed point-wise convolutions misc High-parameter-efficiency convolutions High-parameter-efficiency convolutional neural networks
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topic_title	004 ASE 54.72 bkl High-parameter-efficiency convolutional neural networks Mobile CNNs (dpeaa)DE-He213 Multiple group reused convolutions (dpeaa)DE-He213 Decomposed point-wise convolutions (dpeaa)DE-He213 High-parameter-efficiency convolutions (dpeaa)DE-He213
topic	ddc 004 bkl 54.72 misc Mobile CNNs misc Multiple group reused convolutions misc Decomposed point-wise convolutions misc High-parameter-efficiency convolutions
topic_unstemmed	ddc 004 bkl 54.72 misc Mobile CNNs misc Multiple group reused convolutions misc Decomposed point-wise convolutions misc High-parameter-efficiency convolutions
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title	High-parameter-efficiency convolutional neural networks
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title_full	High-parameter-efficiency convolutional neural networks
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author_browse	Lu, Yao Lu, Guangming Li, Jinxing Xu, Yuanrong Zhang, David
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title_sort	high-parameter-efficiency convolutional neural networks
title_auth	High-parameter-efficiency convolutional neural networks
abstract	Abstract Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper proposes two novel convolutional kernels, multiple group reused convolutions (MGRCs) and decomposed point-wise convolutions (DPCs), to improve the efficiency of parameters by removing the parameter’s redundancy. The summation of MGRC and DPC is called high-parameter-efficiency convolutions (HPEC) in this paper, and the relevant CNNs can be called HPE-CNNs. Experimental results showed that, compared with the traditional convolutional kernels, HPEC can greatly decrease the model size without affecting the performance. Additionally, since the HPE-CNNs reduce the redundancy of parameters more thoroughly than the other mobile CNNs, they can address the over-fitting problem more effectively on the challenging datasets with less abundant training information.
abstractGer	Abstract Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper proposes two novel convolutional kernels, multiple group reused convolutions (MGRCs) and decomposed point-wise convolutions (DPCs), to improve the efficiency of parameters by removing the parameter’s redundancy. The summation of MGRC and DPC is called high-parameter-efficiency convolutions (HPEC) in this paper, and the relevant CNNs can be called HPE-CNNs. Experimental results showed that, compared with the traditional convolutional kernels, HPEC can greatly decrease the model size without affecting the performance. Additionally, since the HPE-CNNs reduce the redundancy of parameters more thoroughly than the other mobile CNNs, they can address the over-fitting problem more effectively on the challenging datasets with less abundant training information.
abstract_unstemmed	Abstract Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper proposes two novel convolutional kernels, multiple group reused convolutions (MGRCs) and decomposed point-wise convolutions (DPCs), to improve the efficiency of parameters by removing the parameter’s redundancy. The summation of MGRC and DPC is called high-parameter-efficiency convolutions (HPEC) in this paper, and the relevant CNNs can be called HPE-CNNs. Experimental results showed that, compared with the traditional convolutional kernels, HPEC can greatly decrease the model size without affecting the performance. Additionally, since the HPE-CNNs reduce the redundancy of parameters more thoroughly than the other mobile CNNs, they can address the over-fitting problem more effectively on the challenging datasets with less abundant training information.
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container_issue	14
title_short	High-parameter-efficiency convolutional neural networks
url	https://dx.doi.org/10.1007/s00521-019-04596-w
remote_bool	true
author2	Lu, Guangming Li, Jinxing Xu, Yuanrong Zhang, David
author2Str	Lu, Guangming Li, Jinxing Xu, Yuanrong Zhang, David
ppnlink	271595574
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s00521-019-04596-w
up_date	2024-07-03T14:19:05.674Z
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