딥러닝을 통한 하이엔드 패션 브랜드 감성 학습

Information

Title 딥러닝을 통한 하이엔드 패션 브랜드 감성 학습
Authors
Seyoon Jang, Ha Youn Kim, Yuri Lee, Jinseok Seol, Seongjae Kim, Sang-goo Lee
Year 2022 / 2
Keywords 패션 브랜드 감성, 하이엔드 브랜드, 이미지 분류, 지도 학습, 딥러닝
Publication Type Domestic Journal
Publication 한국의류학회지, Volume 46, Issue 1, pp. 165-181
Link url

Abstract

The fashion industry is creating innovative business models using artificial intelligence. To efficiently utilize artificial intelligence (AI), fashion data must be classified. Until now, such data have been classified focusing only on the objective properties of fashion products. Their subjective attributes, such as fashion brand sensibilities, are holistic and heuristic intuitions created by a combination of design elements. This study aims to improve the performance of collaborative filtering in the fashion industry by extracting fashion brand sensibility using computer vision technology. The image data set of fashion brand sensibility consists of high-end fashion brand photos that share sensibilities and communicate well in fashion. About 26,000 fashion photos of 11 high-end fashion brand sensibility labels have been collected from the 16FW to 21SS runway and 50 years of US Vogue magazines beginning from 1971. We use EfficientNet-B1 to establish the main architecture and fine-tune the network with ImageNet-ILSVRC. After training fashion brand sensibilities through deep learning, the proposed model achieved an F-1 score of 74% on accuracy tests. Furthermore, as a result of comparing AI machine and human experts, the proposed model is expected to be expanded to mass fashion brands.