Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy
Abstract Traditional NAS methods improve performance by sacrificing the landing ability of the architecture and the re-parameterization technology is expected to solve this problem. However, most current Rep methods rely on prior knowledge to select the re-parameterization operations, which limits t...
Ausführliche Beschreibung
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| Autor*in: |
Yu, Xinyi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 55(2023), 7 vom: 10. März, Seite 8903-8926 |
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| Übergeordnetes Werk: |
volume:55 ; year:2023 ; number:7 ; day:10 ; month:03 ; pages:8903-8926 |
| Links: |
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| DOI / URN: |
10.1007/s11063-023-11184-6 |
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| Katalog-ID: |
SPR053707362 |
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| 520 | |a Abstract Traditional NAS methods improve performance by sacrificing the landing ability of the architecture and the re-parameterization technology is expected to solve this problem. However, most current Rep methods rely on prior knowledge to select the re-parameterization operations, which limits the architecture performance to the type of operations and prior knowledge. At same time, some re-parameterization operations hinder the optimization of the network. To break these restrictions, in this work, an improved re-parameterization search space is designed, including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further enhanced by the search space. An automatic re-parameterization enhancement strategy is designed to effectively explore this search space based on neural architecture search (NAS), which can search an excellent re-parameterization architecture. Then, we solved the optimization problem caused by using some re-parameterization operations to enhance ResNet-style network. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k. | ||
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| 650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Rong, Jintao |4 aut | |
| 700 | 1 | |a Zhang, Mingyang |4 aut | |
| 700 | 1 | |a Ou, Linlin |4 aut | |
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10.1007/s11063-023-11184-6 doi (DE-627)SPR053707362 (SPR)s11063-023-11184-6-e DE-627 ger DE-627 rakwb eng Yu, Xinyi verfasserin aut Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traditional NAS methods improve performance by sacrificing the landing ability of the architecture and the re-parameterization technology is expected to solve this problem. However, most current Rep methods rely on prior knowledge to select the re-parameterization operations, which limits the architecture performance to the type of operations and prior knowledge. At same time, some re-parameterization operations hinder the optimization of the network. To break these restrictions, in this work, an improved re-parameterization search space is designed, including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further enhanced by the search space. An automatic re-parameterization enhancement strategy is designed to effectively explore this search space based on neural architecture search (NAS), which can search an excellent re-parameterization architecture. Then, we solved the optimization problem caused by using some re-parameterization operations to enhance ResNet-style network. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k. Neural network (dpeaa)DE-He213 Evolutionary algorithm (dpeaa)DE-He213 Structural re-parameterization (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wang, Xiaowei aut Rong, Jintao aut Zhang, Mingyang aut Ou, Linlin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 55(2023), 7 vom: 10. März, Seite 8903-8926 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:55 year:2023 number:7 day:10 month:03 pages:8903-8926 https://dx.doi.org/10.1007/s11063-023-11184-6 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2023 7 10 03 8903-8926 |
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10.1007/s11063-023-11184-6 doi (DE-627)SPR053707362 (SPR)s11063-023-11184-6-e DE-627 ger DE-627 rakwb eng Yu, Xinyi verfasserin aut Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traditional NAS methods improve performance by sacrificing the landing ability of the architecture and the re-parameterization technology is expected to solve this problem. However, most current Rep methods rely on prior knowledge to select the re-parameterization operations, which limits the architecture performance to the type of operations and prior knowledge. At same time, some re-parameterization operations hinder the optimization of the network. To break these restrictions, in this work, an improved re-parameterization search space is designed, including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further enhanced by the search space. An automatic re-parameterization enhancement strategy is designed to effectively explore this search space based on neural architecture search (NAS), which can search an excellent re-parameterization architecture. Then, we solved the optimization problem caused by using some re-parameterization operations to enhance ResNet-style network. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k. Neural network (dpeaa)DE-He213 Evolutionary algorithm (dpeaa)DE-He213 Structural re-parameterization (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wang, Xiaowei aut Rong, Jintao aut Zhang, Mingyang aut Ou, Linlin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 55(2023), 7 vom: 10. März, Seite 8903-8926 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:55 year:2023 number:7 day:10 month:03 pages:8903-8926 https://dx.doi.org/10.1007/s11063-023-11184-6 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2023 7 10 03 8903-8926 |
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10.1007/s11063-023-11184-6 doi (DE-627)SPR053707362 (SPR)s11063-023-11184-6-e DE-627 ger DE-627 rakwb eng Yu, Xinyi verfasserin aut Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traditional NAS methods improve performance by sacrificing the landing ability of the architecture and the re-parameterization technology is expected to solve this problem. However, most current Rep methods rely on prior knowledge to select the re-parameterization operations, which limits the architecture performance to the type of operations and prior knowledge. At same time, some re-parameterization operations hinder the optimization of the network. To break these restrictions, in this work, an improved re-parameterization search space is designed, including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further enhanced by the search space. An automatic re-parameterization enhancement strategy is designed to effectively explore this search space based on neural architecture search (NAS), which can search an excellent re-parameterization architecture. Then, we solved the optimization problem caused by using some re-parameterization operations to enhance ResNet-style network. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k. Neural network (dpeaa)DE-He213 Evolutionary algorithm (dpeaa)DE-He213 Structural re-parameterization (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wang, Xiaowei aut Rong, Jintao aut Zhang, Mingyang aut Ou, Linlin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 55(2023), 7 vom: 10. März, Seite 8903-8926 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:55 year:2023 number:7 day:10 month:03 pages:8903-8926 https://dx.doi.org/10.1007/s11063-023-11184-6 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2023 7 10 03 8903-8926 |
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10.1007/s11063-023-11184-6 doi (DE-627)SPR053707362 (SPR)s11063-023-11184-6-e DE-627 ger DE-627 rakwb eng Yu, Xinyi verfasserin aut Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traditional NAS methods improve performance by sacrificing the landing ability of the architecture and the re-parameterization technology is expected to solve this problem. However, most current Rep methods rely on prior knowledge to select the re-parameterization operations, which limits the architecture performance to the type of operations and prior knowledge. At same time, some re-parameterization operations hinder the optimization of the network. To break these restrictions, in this work, an improved re-parameterization search space is designed, including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further enhanced by the search space. An automatic re-parameterization enhancement strategy is designed to effectively explore this search space based on neural architecture search (NAS), which can search an excellent re-parameterization architecture. Then, we solved the optimization problem caused by using some re-parameterization operations to enhance ResNet-style network. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k. Neural network (dpeaa)DE-He213 Evolutionary algorithm (dpeaa)DE-He213 Structural re-parameterization (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wang, Xiaowei aut Rong, Jintao aut Zhang, Mingyang aut Ou, Linlin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 55(2023), 7 vom: 10. März, Seite 8903-8926 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:55 year:2023 number:7 day:10 month:03 pages:8903-8926 https://dx.doi.org/10.1007/s11063-023-11184-6 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2023 7 10 03 8903-8926 |
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10.1007/s11063-023-11184-6 doi (DE-627)SPR053707362 (SPR)s11063-023-11184-6-e DE-627 ger DE-627 rakwb eng Yu, Xinyi verfasserin aut Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traditional NAS methods improve performance by sacrificing the landing ability of the architecture and the re-parameterization technology is expected to solve this problem. However, most current Rep methods rely on prior knowledge to select the re-parameterization operations, which limits the architecture performance to the type of operations and prior knowledge. At same time, some re-parameterization operations hinder the optimization of the network. To break these restrictions, in this work, an improved re-parameterization search space is designed, including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further enhanced by the search space. An automatic re-parameterization enhancement strategy is designed to effectively explore this search space based on neural architecture search (NAS), which can search an excellent re-parameterization architecture. Then, we solved the optimization problem caused by using some re-parameterization operations to enhance ResNet-style network. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k. Neural network (dpeaa)DE-He213 Evolutionary algorithm (dpeaa)DE-He213 Structural re-parameterization (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wang, Xiaowei aut Rong, Jintao aut Zhang, Mingyang aut Ou, Linlin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 55(2023), 7 vom: 10. März, Seite 8903-8926 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:55 year:2023 number:7 day:10 month:03 pages:8903-8926 https://dx.doi.org/10.1007/s11063-023-11184-6 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2023 7 10 03 8903-8926 |
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Yu, Xinyi |
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efficient re-parameterization operations search for easy-to-deploy network based on directional evolutionary strategy |
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Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy |
| abstract |
Abstract Traditional NAS methods improve performance by sacrificing the landing ability of the architecture and the re-parameterization technology is expected to solve this problem. However, most current Rep methods rely on prior knowledge to select the re-parameterization operations, which limits the architecture performance to the type of operations and prior knowledge. At same time, some re-parameterization operations hinder the optimization of the network. To break these restrictions, in this work, an improved re-parameterization search space is designed, including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further enhanced by the search space. An automatic re-parameterization enhancement strategy is designed to effectively explore this search space based on neural architecture search (NAS), which can search an excellent re-parameterization architecture. Then, we solved the optimization problem caused by using some re-parameterization operations to enhance ResNet-style network. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Traditional NAS methods improve performance by sacrificing the landing ability of the architecture and the re-parameterization technology is expected to solve this problem. However, most current Rep methods rely on prior knowledge to select the re-parameterization operations, which limits the architecture performance to the type of operations and prior knowledge. At same time, some re-parameterization operations hinder the optimization of the network. To break these restrictions, in this work, an improved re-parameterization search space is designed, including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further enhanced by the search space. An automatic re-parameterization enhancement strategy is designed to effectively explore this search space based on neural architecture search (NAS), which can search an excellent re-parameterization architecture. Then, we solved the optimization problem caused by using some re-parameterization operations to enhance ResNet-style network. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Traditional NAS methods improve performance by sacrificing the landing ability of the architecture and the re-parameterization technology is expected to solve this problem. However, most current Rep methods rely on prior knowledge to select the re-parameterization operations, which limits the architecture performance to the type of operations and prior knowledge. At same time, some re-parameterization operations hinder the optimization of the network. To break these restrictions, in this work, an improved re-parameterization search space is designed, including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further enhanced by the search space. An automatic re-parameterization enhancement strategy is designed to effectively explore this search space based on neural architecture search (NAS), which can search an excellent re-parameterization architecture. Then, we solved the optimization problem caused by using some re-parameterization operations to enhance ResNet-style network. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy |
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https://dx.doi.org/10.1007/s11063-023-11184-6 |
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| author2 |
Wang, Xiaowei Rong, Jintao Zhang, Mingyang Ou, Linlin |
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Wang, Xiaowei Rong, Jintao Zhang, Mingyang Ou, Linlin |
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10.1007/s11063-023-11184-6 |
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2024-07-03T21:27:15.827Z |
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| score |
7.4022045 |