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ERIC Number: EJ1292092
Record Type: Journal
Publication Date: 2021-Apr
Pages: 20
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0957 7572
EISSN: N/A
Available Date: N/A
Creative Idea Generation Method Based on Deep Learning Technology
Zhao, Tianjiao; Yang, Junyu; Zhang, Hechen; Siu, Kin Wai Michael
International Journal of Technology and Design Education, v31 n2 p421-440 Apr 2021
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.
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A