New Product Design with Popular Fashion Style Discovery Using Machine Learning

  • Jiating Zhu
  • Yu YangEmail author
  • Jiannong Cao
  • Esther Chak Fung Mei
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)


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.


Fashion style discovery Deep learning VAE generator 



The work is partially supported by the funding for Project of Strategic Importance provided by The Hong Kong Polytechnic University (Project Code: 1-ZE26), funding for the demonstration project on large data provided by The Hong Kong Polytechnic University (project account code: 9A5 V), and RGC General Research Fund, PolyU 152199/17E.


  1. 1.
    Brahmadeep, Thomassey, S.: Intelligent demand forecasting systems for fast fashion. In: Information Systems for the Fashion and Apparel Industry, pp. 145–161 (2016)CrossRefGoogle Scholar
  2. 2.
    Banica, L., Hagiu, A.: Using big data analytics to improve decision-making in apparel supply chains. In: Information Systems for the Fashion and Apparel Industry, pp. 63–95 (2016)CrossRefGoogle Scholar
  3. 3.
    Shankar, D., Narumanchi, S., Ananya, H.-A., Kompalli, P., Chaudhury, K.: Deep learning based large scale visual recommendation and search for e-commerce (2017). arXiv preprint arXiv: 1703.02344Google Scholar
  4. 4.
    Al-Halah, Z., Stiefelhagen, R., Grauman, K.: Fashion forward: Forecasting visual style in fashion. In: ICCV, pp. 388–397 (2017)Google Scholar
  5. 5.
    Deverall, J., Lee, J., Ayala, M.: Using generative adversarial networks to design shoes: the preliminary steps, CS231n in Stanford (2017)Google Scholar
  6. 6.
    Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X.: DeepFashion: powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1096–1104 (2016)Google Scholar
  7. 7.
    Doersch, C.: Tutorial on variational autoencoders, (2016) arXiv preprint arXiv: 1606.05908Google Scholar
  8. 8.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.-C., Bengio, Y.: Generative adversarial nets. In: Proceedings of NIPS, pp. 2672–2680 (2014)Google Scholar
  9. 9.
    Kingma, D.-P., Welling, M.: Auto-encoding variational Bayes. In: ICLR (2014)Google Scholar
  10. 10.
    Variational Auto Encoder with Concrete Latent Distribution. Accessed 28 Mar 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jiating Zhu
    • 1
  • Yu Yang
    • 1
    Email author
  • Jiannong Cao
    • 1
  • Esther Chak Fung Mei
    • 1
  1. 1.The Hong Kong Polytechnic UniversityHong KongChina

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