The Study of Fashion Items Generation Based on Big Data and Deep Learning

Yang Song; Zhijian Wang; Jianming Zhang; Yan Wang

doi:10.57118/creosar/978-1-915740-01-4_1

Authors

Yang Song
Zhijian Wang
Jianming Zhang
Yan Wang

DOI:

https://doi.org/10.57118/creosar/978-1-915740-01-4_1

Keywords:

Fashion Design, Big Data, Generative Adversarial Networks, Disentangled Generator

Abstract

Since the Third Industrial Revolution, computer network information technology has made continuous progress, and the era of big data and Artificial Intelligence (AI) has arrived. The rapid progress of big data and AI will not only be able to promote the continuous improvement of computer network information technology, but also improve the level of economic development and make positive contributions. In this chapter, we propose a style-based fashion items generator for clothing design with big data and deep learning. Designing a clothing item is complicated, time-consuming, and challenging for clothes designers and clothing industry; however, recent improvements in conditional image generation provide a feasible solution. With a desired fashion category, the proposed framework will generate a nonexistent fashion item which cannot be distinguished from the real ones. Generative adversarial networks (GANs) make it possible to perform our clothing design with semi-supervised conditions. Our method can generate clothing items conditioned on 15 fashion categories. Furthermore, due to the multimodality of the clothes images, we employ a style-based generator with disentangled networks and adopt a multistage discriminator to improve the results of image generation. The effectiveness of our approach is well demonstrated through quantitative experiments. Our method will spark inspirations for fashion designers in their work.

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