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Generation of fashionable clothes using generative adversarial networks: A preliminary feasibility study

Montek Singh (School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, India)
Utkarsh Bajpai (School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, India)
Vijayarajan V. (School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, India)
Surya Prasath (Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA) (Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio, USA) (Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio, USA)

International Journal of Clothing Science and Technology

ISSN: 0955-6222

Article publication date: 21 August 2019

Issue publication date: 22 April 2020

Abstract

Purpose

There are various style options available when one buys clothes on online shopping websites, however the availability the new fashion trends or choices require further user interaction in generating fashionable clothes. The paper aims to discuss this issue.

Design/methodology/approach

Based on generative adversarial networks (GANs) from the deep learning paradigm, here the authors suggest model system that will take the latest fashion trends and the clothes bought by users as input and generate new clothes. The new set of clothes will be based on trending fashion but at the same time will have attributes of clothes where were bought by the consumer earlier.

Findings

In the proposed machine learning based approach, the clothes generated by the system will personalized for different types of consumers. This will help the manufacturing companies to come up with the designs, which will directly target the customer.

Research limitations/implications

The biggest limitation of the collected data set is that the clothes in the two domains do not belong to a specific category. For instance the vintage clothes data set has coats, dresses, skirts, etc. These different types of clothes are not segregated. Also there is no restriction on the number of images of each type of cloth. There can many images of dresses and only a few for the coats. This can affect the end results. The aim of the paper was to find whether new and desirable clothes can be created from two different domains or not. Analyzing the impact of “the number of images for each class of cloth” is something which is aim to work in future.

Practical implications

The authors believe such personalized experience can increase the sales of fashion stores and here provide the feasibility of such a clothes generation system.

Originality/value

Applying GANs from the deep learning models for generating fashionable clothes.

Keywords

Acknowledgements

The authors would like to thank NVIDIA for providing their TITAN X GPU without which this research would not be possible. The authors also thank the reviewer for the comments which helped improve the work.

Citation

Singh, M., Bajpai, U., V., V. and Prasath, S. (2020), "Generation of fashionable clothes using generative adversarial networks: A preliminary feasibility study", International Journal of Clothing Science and Technology, Vol. 32 No. 2, pp. 177-187. https://doi.org/10.1108/IJCST-12-2018-0148

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited