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Exploring Latent Space Using a Non-linear Dimensionality Reduction Algorithm for Style Transfer Application

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New Trends in Database and Information Systems (ADBIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1652))

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Abstract

A latent space represents data by embedding them in a multidimensional vector space. In this way an abstract estimation of any complex domain could be created. Empirical approach for exploring the latent space generated by known pre-trained model of human face images using a nonlinear dimensionality reduction algorithm is presented in this paper. One aim was to find more detailed entangled features (beard and hair color) between the real images and their representation, in artistic face portrait application. Experimental results showed that sparse vectors in the latent space could be useful to obtain optimal results with relatively low effort. To evaluate our work, we present the results of a survey that was sent to 25 thousand subscribers of the real world application and got around 360 responses. The main goal of the survey was to find some quantitative measurements that can be used in our research.

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Notes

  1. 1.

    DaVinciFace is a software registered at SIAE (Italian Authors’ and Publishers’ Association) - www.davinciface.com.

  2. 2.

    SME: small-to-medium enterprise.

  3. 3.

    paperswithcode.com/dataset/ffhq, last visite 14/05/2022.

  4. 4.

    www.surveyhero.com is a software to design, collect and analyze responses of surveys.

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Author information

Authors and Affiliations

  1. Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy

    Doaa Almhaithawi

  2. Mathema, Firenze, Italy

    Alessandro Bellini & Stefano Cuomo

Authors
  1. Doaa Almhaithawi
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  2. Alessandro Bellini
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  3. Stefano Cuomo
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Corresponding author

Correspondence to Doaa Almhaithawi .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  2. Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  3. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  5. University of Genoa, Genoa, Italy

    Barbara Catania

  6. CNRS, Villeurbanne Cedex, France

    Genoveva Vargas-Solar

  7. University of Calabria, Rende, Italy

    Ester Zumpano

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Almhaithawi, D., Bellini, A., Cuomo, S. (2022). Exploring Latent Space Using a Non-linear Dimensionality Reduction Algorithm for Style Transfer Application. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_26

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  • DOI: https://doi.org/10.1007/978-3-031-15743-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15742-4

  • Online ISBN: 978-3-031-15743-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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