New product design with popular fashion style discovery using machine learning

Jiating Zhu, Yu Yang, Jiannong Cao, Esther Chak Fung Mei

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Fashion companies have always been facing a critical issue to design products that fit consumers’ needs. On one hand, fashion industries continually reinventing itself. On the other hand, shoppers’ preference is changing from time to time. In this work, we make use of machine learning and computer vision technologies to automatically design new “must-have” fashion products with popular styles discovered from fashion product images and historical transaction data. Products in each discovered style share similar visual attributes and popularity. The visual-based fashion attributes are learned from fashion product images via a deep convolutional neural network (CNN). Fusing together with popularity attributes extracted from transaction data, a set of styles is discovered by Nonnegative matrix factorization(NMF). Eventually, new fashion products are generated from the discovered styles by Variational Autoencoder (VAE). The result shows that our method can successfully generate combinations of interpretable elements from different popular fashion products. We believe this work has the potential to be applied in the fashion industry to help to keep reasonable stocks of goods and capture most profits.

Original languageEnglish
Title of host publicationArtificial Intelligence on Fashion and Textiles - Proceedings of the Artificial Intelligence on Fashion and Textiles AIFT Conference 2018
EditorsWai Keung Wong
PublisherSpringer-Verlag
Pages121-128
Number of pages8
ISBN (Print)9783319996943
DOIs
Publication statusPublished - 1 Jan 2019
EventArtificial Intelligence on Fashion and Textiles Conference, AIFT 2018 - Hong Kong, China
Duration: 27 Jun 201829 Jun 2018

Publication series

NameAdvances in Intelligent Systems and Computing
Volume849
ISSN (Print)2194-5357

Conference

ConferenceArtificial Intelligence on Fashion and Textiles Conference, AIFT 2018
CountryChina
CityHong Kong
Period27/06/1829/06/18

Keywords

  • Deep learning
  • Fashion style discovery
  • VAE generator

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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