Sportive Fashion Trend Reports: A Hybrid Style Analysis Based on Deep Learning Techniques

Hyosun An, Sunghoon Kim and Yerim Choi
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Hyosun An: Department of Fashion Industry, Ewha Womans University, Seoul 03760, Korea
Sunghoon Kim: Department of Data Science, Seoul Women’s University, Seoul 01797, Korea
Yerim Choi: Department of Data Science, Seoul Women’s University, Seoul 01797, Korea

Sustainability, 2021, vol. 13, issue 17, 1-16

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.

Keywords: sportive fashion; fashion trend; hybrid style; ML-GCN; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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