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Sportive Fashion Trend Reports: A Hybrid Style Analysis Based on Deep Learning Techniques
- Source :
- Sustainability, Vol 13, Iss 9530, p 9530 (2021), Sustainability, Volume 13, Issue 17
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- This study aimed to use quantitative methods and deep learning techniques to report sportive fashion trends. We collected sportive fashion images from fashion collections of the past decades and utilized the multi-label graph convolutional network (ML-GCN) model to detect and explore hybrid styles. Based on the literature review, we proposed a theoretical framework to investigate sportive fashion trends. The ML-GCN was designed to classify five style categories, “street,” “retro,” “sexy,” “modern,” and “sporty,” and the predictive probabilities of the five styles of fashion images were extracted. We statistically validated the hybrid style results derived from the ML-GCN model and suggested an application method of deep learning-based trend reports in the fashion industry. This study reported sportive fashion by hybrid style dependency, forecasting, and brand clustering. We visualized the predicted probability for a hybrid style to a three-dimensional scale expected to help designers and researchers in the field of fashion to achieve digital design innovation cooperating with deep learning techniques.
- Subjects :
- Dependency (UML)
Computer science
Geography, Planning and Development
TJ807-830
Management, Monitoring, Policy and Law
hybrid style
Machine learning
computer.software_genre
TD194-195
ML-GCN
Field (computer science)
Renewable energy sources
Style (sociolinguistics)
Style analysis
GE1-350
Cluster analysis
Environmental effects of industries and plants
Renewable Energy, Sustainability and the Environment
business.industry
Deep learning
fashion trend
deep learning
Environmental sciences
sportive fashion
Graph (abstract data type)
Artificial intelligence
Scale (map)
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 20711050
- Volume :
- 13
- Issue :
- 9530
- Database :
- OpenAIRE
- Journal :
- Sustainability
- Accession number :
- edsair.doi.dedup.....9587b8a20dd3110da29c767e929f7f2f