Creative idea generation method based on deep learning technology

Abstract Generating creative ideas is critical in the design process. Currently, massive amounts of design data are existing and effective use of data can stimulate inspiration. However, there has been relatively little research on large-scale design image materials and creative knowledge mining. He...
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Autor*in:	Zhao, Tianjiao [verfasserIn] Yang, Junyu [verfasserIn] Zhang, Hechen [verfasserIn] Siu, Kin Wai Michael [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Creative idea generation Deep learning Big data Computer-aided innovative design

Übergeordnetes Werk:	Enthalten in: International journal of technology and design education - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990, 31(2019), 2 vom: 11. Dez., Seite 421-440
Übergeordnetes Werk:	volume:31 ; year:2019 ; number:2 ; day:11 ; month:12 ; pages:421-440

Links:	Volltext

DOI / URN:	10.1007/s10798-019-09556-y

Katalog-ID:	SPR043583407

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520			\|a Abstract Generating creative ideas is critical in the design process. Currently, massive amounts of design data are existing and effective use of data can stimulate inspiration. However, there has been relatively little research on large-scale design image materials and creative knowledge mining. Here we report a creative idea generation method based on deep learning technology. Firstly, we identified the most effective point for presenting image stimuli for inspiration. Then we used artificial selection to construct a substantial database of highly creative image stimuli. Based on the selected images, we used canonical correlation analysis and convolutional neural networks to learn two projections to search for highly creative images in a logo database. The proposed method combines design theory and computational techniques, providing a new creative design thinking method for identifying appropriate stimuli in large databases.
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allfields	10.1007/s10798-019-09556-y doi (DE-627)SPR043583407 (DE-599)SPRs10798-019-09556-y-e (SPR)s10798-019-09556-y-e DE-627 ger DE-627 rakwb eng 540 ASE 50.04 bkl Zhao, Tianjiao verfasserin aut Creative idea generation method based on deep learning technology 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Generating creative ideas is critical in the design process. Currently, massive amounts of design data are existing and effective use of data can stimulate inspiration. However, there has been relatively little research on large-scale design image materials and creative knowledge mining. Here we report a creative idea generation method based on deep learning technology. Firstly, we identified the most effective point for presenting image stimuli for inspiration. Then we used artificial selection to construct a substantial database of highly creative image stimuli. Based on the selected images, we used canonical correlation analysis and convolutional neural networks to learn two projections to search for highly creative images in a logo database. The proposed method combines design theory and computational techniques, providing a new creative design thinking method for identifying appropriate stimuli in large databases. Creative idea generation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Computer-aided innovative design (dpeaa)DE-He213 Yang, Junyu verfasserin aut Zhang, Hechen verfasserin aut Siu, Kin Wai Michael verfasserin aut Enthalten in International journal of technology and design education Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 31(2019), 2 vom: 11. Dez., Seite 421-440 (DE-627)315272694 (DE-600)2016164-5 1573-1804 nnns volume:31 year:2019 number:2 day:11 month:12 pages:421-440 https://dx.doi.org/10.1007/s10798-019-09556-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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 50.04 ASE AR 31 2019 2 11 12 421-440
spelling	10.1007/s10798-019-09556-y doi (DE-627)SPR043583407 (DE-599)SPRs10798-019-09556-y-e (SPR)s10798-019-09556-y-e DE-627 ger DE-627 rakwb eng 540 ASE 50.04 bkl Zhao, Tianjiao verfasserin aut Creative idea generation method based on deep learning technology 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Generating creative ideas is critical in the design process. Currently, massive amounts of design data are existing and effective use of data can stimulate inspiration. However, there has been relatively little research on large-scale design image materials and creative knowledge mining. Here we report a creative idea generation method based on deep learning technology. Firstly, we identified the most effective point for presenting image stimuli for inspiration. Then we used artificial selection to construct a substantial database of highly creative image stimuli. Based on the selected images, we used canonical correlation analysis and convolutional neural networks to learn two projections to search for highly creative images in a logo database. The proposed method combines design theory and computational techniques, providing a new creative design thinking method for identifying appropriate stimuli in large databases. Creative idea generation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Computer-aided innovative design (dpeaa)DE-He213 Yang, Junyu verfasserin aut Zhang, Hechen verfasserin aut Siu, Kin Wai Michael verfasserin aut Enthalten in International journal of technology and design education Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 31(2019), 2 vom: 11. Dez., Seite 421-440 (DE-627)315272694 (DE-600)2016164-5 1573-1804 nnns volume:31 year:2019 number:2 day:11 month:12 pages:421-440 https://dx.doi.org/10.1007/s10798-019-09556-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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 50.04 ASE AR 31 2019 2 11 12 421-440
allfields_unstemmed	10.1007/s10798-019-09556-y doi (DE-627)SPR043583407 (DE-599)SPRs10798-019-09556-y-e (SPR)s10798-019-09556-y-e DE-627 ger DE-627 rakwb eng 540 ASE 50.04 bkl Zhao, Tianjiao verfasserin aut Creative idea generation method based on deep learning technology 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Generating creative ideas is critical in the design process. Currently, massive amounts of design data are existing and effective use of data can stimulate inspiration. However, there has been relatively little research on large-scale design image materials and creative knowledge mining. Here we report a creative idea generation method based on deep learning technology. Firstly, we identified the most effective point for presenting image stimuli for inspiration. Then we used artificial selection to construct a substantial database of highly creative image stimuli. Based on the selected images, we used canonical correlation analysis and convolutional neural networks to learn two projections to search for highly creative images in a logo database. The proposed method combines design theory and computational techniques, providing a new creative design thinking method for identifying appropriate stimuli in large databases. Creative idea generation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Computer-aided innovative design (dpeaa)DE-He213 Yang, Junyu verfasserin aut Zhang, Hechen verfasserin aut Siu, Kin Wai Michael verfasserin aut Enthalten in International journal of technology and design education Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 31(2019), 2 vom: 11. Dez., Seite 421-440 (DE-627)315272694 (DE-600)2016164-5 1573-1804 nnns volume:31 year:2019 number:2 day:11 month:12 pages:421-440 https://dx.doi.org/10.1007/s10798-019-09556-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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 50.04 ASE AR 31 2019 2 11 12 421-440
allfieldsGer	10.1007/s10798-019-09556-y doi (DE-627)SPR043583407 (DE-599)SPRs10798-019-09556-y-e (SPR)s10798-019-09556-y-e DE-627 ger DE-627 rakwb eng 540 ASE 50.04 bkl Zhao, Tianjiao verfasserin aut Creative idea generation method based on deep learning technology 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Generating creative ideas is critical in the design process. Currently, massive amounts of design data are existing and effective use of data can stimulate inspiration. However, there has been relatively little research on large-scale design image materials and creative knowledge mining. Here we report a creative idea generation method based on deep learning technology. Firstly, we identified the most effective point for presenting image stimuli for inspiration. Then we used artificial selection to construct a substantial database of highly creative image stimuli. Based on the selected images, we used canonical correlation analysis and convolutional neural networks to learn two projections to search for highly creative images in a logo database. The proposed method combines design theory and computational techniques, providing a new creative design thinking method for identifying appropriate stimuli in large databases. Creative idea generation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Computer-aided innovative design (dpeaa)DE-He213 Yang, Junyu verfasserin aut Zhang, Hechen verfasserin aut Siu, Kin Wai Michael verfasserin aut Enthalten in International journal of technology and design education Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 31(2019), 2 vom: 11. Dez., Seite 421-440 (DE-627)315272694 (DE-600)2016164-5 1573-1804 nnns volume:31 year:2019 number:2 day:11 month:12 pages:421-440 https://dx.doi.org/10.1007/s10798-019-09556-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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 50.04 ASE AR 31 2019 2 11 12 421-440
allfieldsSound	10.1007/s10798-019-09556-y doi (DE-627)SPR043583407 (DE-599)SPRs10798-019-09556-y-e (SPR)s10798-019-09556-y-e DE-627 ger DE-627 rakwb eng 540 ASE 50.04 bkl Zhao, Tianjiao verfasserin aut Creative idea generation method based on deep learning technology 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Generating creative ideas is critical in the design process. Currently, massive amounts of design data are existing and effective use of data can stimulate inspiration. However, there has been relatively little research on large-scale design image materials and creative knowledge mining. Here we report a creative idea generation method based on deep learning technology. Firstly, we identified the most effective point for presenting image stimuli for inspiration. Then we used artificial selection to construct a substantial database of highly creative image stimuli. Based on the selected images, we used canonical correlation analysis and convolutional neural networks to learn two projections to search for highly creative images in a logo database. The proposed method combines design theory and computational techniques, providing a new creative design thinking method for identifying appropriate stimuli in large databases. Creative idea generation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Computer-aided innovative design (dpeaa)DE-He213 Yang, Junyu verfasserin aut Zhang, Hechen verfasserin aut Siu, Kin Wai Michael verfasserin aut Enthalten in International journal of technology and design education Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 31(2019), 2 vom: 11. Dez., Seite 421-440 (DE-627)315272694 (DE-600)2016164-5 1573-1804 nnns volume:31 year:2019 number:2 day:11 month:12 pages:421-440 https://dx.doi.org/10.1007/s10798-019-09556-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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 50.04 ASE AR 31 2019 2 11 12 421-440
language	English
source	Enthalten in International journal of technology and design education 31(2019), 2 vom: 11. Dez., Seite 421-440 volume:31 year:2019 number:2 day:11 month:12 pages:421-440
sourceStr	Enthalten in International journal of technology and design education 31(2019), 2 vom: 11. Dez., Seite 421-440 volume:31 year:2019 number:2 day:11 month:12 pages:421-440
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Creative idea generation Deep learning Big data Computer-aided innovative design
dewey-raw	540
isfreeaccess_bool	false
container_title	International journal of technology and design education
authorswithroles_txt_mv	Zhao, Tianjiao @@aut@@ Yang, Junyu @@aut@@ Zhang, Hechen @@aut@@ Siu, Kin Wai Michael @@aut@@
publishDateDaySort_date	2019-12-11T00:00:00Z
hierarchy_top_id	315272694
dewey-sort	3540
id	SPR043583407
language_de	englisch
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author	Zhao, Tianjiao
spellingShingle	Zhao, Tianjiao ddc 540 bkl 50.04 misc Creative idea generation misc Deep learning misc Big data misc Computer-aided innovative design Creative idea generation method based on deep learning technology
authorStr	Zhao, Tianjiao
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topic_title	540 ASE 50.04 bkl Creative idea generation method based on deep learning technology Creative idea generation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Computer-aided innovative design (dpeaa)DE-He213
topic	ddc 540 bkl 50.04 misc Creative idea generation misc Deep learning misc Big data misc Computer-aided innovative design
topic_unstemmed	ddc 540 bkl 50.04 misc Creative idea generation misc Deep learning misc Big data misc Computer-aided innovative design
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title	Creative idea generation method based on deep learning technology
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title_full	Creative idea generation method based on deep learning technology
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author_browse	Zhao, Tianjiao Yang, Junyu Zhang, Hechen Siu, Kin Wai Michael
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title_sort	creative idea generation method based on deep learning technology
title_auth	Creative idea generation method based on deep learning technology
abstract	Abstract Generating creative ideas is critical in the design process. Currently, massive amounts of design data are existing and effective use of data can stimulate inspiration. However, there has been relatively little research on large-scale design image materials and creative knowledge mining. Here we report a creative idea generation method based on deep learning technology. Firstly, we identified the most effective point for presenting image stimuli for inspiration. Then we used artificial selection to construct a substantial database of highly creative image stimuli. Based on the selected images, we used canonical correlation analysis and convolutional neural networks to learn two projections to search for highly creative images in a logo database. The proposed method combines design theory and computational techniques, providing a new creative design thinking method for identifying appropriate stimuli in large databases.
abstractGer	Abstract Generating creative ideas is critical in the design process. Currently, massive amounts of design data are existing and effective use of data can stimulate inspiration. However, there has been relatively little research on large-scale design image materials and creative knowledge mining. Here we report a creative idea generation method based on deep learning technology. Firstly, we identified the most effective point for presenting image stimuli for inspiration. Then we used artificial selection to construct a substantial database of highly creative image stimuli. Based on the selected images, we used canonical correlation analysis and convolutional neural networks to learn two projections to search for highly creative images in a logo database. The proposed method combines design theory and computational techniques, providing a new creative design thinking method for identifying appropriate stimuli in large databases.
abstract_unstemmed	Abstract Generating creative ideas is critical in the design process. Currently, massive amounts of design data are existing and effective use of data can stimulate inspiration. However, there has been relatively little research on large-scale design image materials and creative knowledge mining. Here we report a creative idea generation method based on deep learning technology. Firstly, we identified the most effective point for presenting image stimuli for inspiration. Then we used artificial selection to construct a substantial database of highly creative image stimuli. Based on the selected images, we used canonical correlation analysis and convolutional neural networks to learn two projections to search for highly creative images in a logo database. The proposed method combines design theory and computational techniques, providing a new creative design thinking method for identifying appropriate stimuli in large databases.
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container_issue	2
title_short	Creative idea generation method based on deep learning technology
url	https://dx.doi.org/10.1007/s10798-019-09556-y
remote_bool	true
author2	Yang, Junyu Zhang, Hechen Siu, Kin Wai Michael
author2Str	Yang, Junyu Zhang, Hechen Siu, Kin Wai Michael
ppnlink	315272694
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s10798-019-09556-y
up_date	2024-07-03T19:35:39.250Z
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