Generation of fashionable clothes using generative adversarial networks

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.

Big data and digital design models for fashion design

“…21,22 Fashion enterprises collect data on fashion sales from websites, stores, and mobile phone apps, among other sources. 23 They intelligently analyze the data and select diverse fashion-attribute data to identify preferences for colors, styles, fabrics, sizes, brands, and so on, among consumers according to age, gender, region, and other characteristics. 24 This approach provides a guide for fashion design and for fashion production managers.…”

“…40 Fashion can be considered a metaphor for identity by which individuals express who they are and who they hope to become. 23 Customers, therefore, want fashion to express their individuality and ideas by way of design elements such as images and colors. 41,42 Hence, there is a need for fashion design systems that help ordinary people design fashions that appeal to them.…”

“…Singh et al 23 studied a model system that can take the clothes bought by consumers and the latest fashion trends as input to generate new fashion. This system can design personalized clothes for consumers, which helping garment enterprises to come up with the fashion for target customers.…”

You can Try without Visiting: A Comprehensive Survey on Virtually Try-on Outfits

“…Recently, many researchers tried to explore and provide solutions for different fashion tasks using the concepts of artificial intelligence. Several works contributed for fashion recommendation in [104,105], object detection and classification [22][23][24][25][26][27][28][29], Image Generation and Manipulation in [101][102][103]. Fig.…”

A survey of deep network techniques all classifiers can adopt

“…Additionally, the neural network community developed unsupervised algorithms to help with the learning of unlabeled data. These unsupervised methods have found their way to real-world applications, such as creating generative adversarial networks (GANs) that design clothes (Singh et al 2020). The term deep has been used to distinguish these networks from shallow networks with only one hidden layer; in contrast, DNNs have multiple hidden layers.…”