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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
DaVinciFace is a software registered at SIAE (Italian Authors’ and Publishers’ Association) - www.davinciface.com.
- 2.
SME: small-to-medium enterprise.
- 3.
paperswithcode.com/dataset/ffhq, last visite 14/05/2022.
- 4.
www.surveyhero.com is a software to design, collect and analyze responses of surveys.
References
Davinciface homepage. https://www.davinciface.com/. Accessed 6 July 2022
Xu, Z., Wilber, M., Fang, C., Hertzmann, A., Jin, H.: Learning from multi-domain artistic images for arbitrary style transfer. arXiv preprint arXiv:1805.09987 (2018)
Shaban, M.T., Baur, C., Navab, N., Albarqouni, S.: StainGAN: stain style transfer for digital histological images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 953–956. IEEE (2019)
Prabhumoye, S., Tsvetkov, Y., Salakhutdinov, R., Black, A.W.: Style transfer through back-translation. arXiv preprint arXiv:1804.09000 (2018)
Ruder, M., Dosovitskiy, A., Brox, T.: Artistic style transfer for videos and spherical images. Int. J. Comput. Vision 126(11), 1199–1219 (2018)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Shen, J., Wang, R., Shen, H.-W.: Visual exploration of latent space for traditional Chinese music. Vis. Inform. 4(2), 99–108 (2020)
Crecchi, F., Bacciu, D., Biggio, B.: Detecting adversarial examples through nonlinear dimensionality reduction. arXiv preprint arXiv:1904.13094 (2019)
Tasoulis, S., Pavlidis, N.G., Roos, T.: Nonlinear dimensionality reduction for clustering. Pattern Recogn. 107, 107508 (2020)
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110–8119 (2020)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)
Chong, M.J., Lee, H.-Y., Forsyth, D.: StyleGAN of all trades: image manipulation with only pretrained StyleGAN. arXiv preprint arXiv:2111.01619 (2021)
Balasubramanian, M., Schwartz, E.L.: The isomap algorithm and topological stability. Science 295(5552), 7–7 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-15743-1_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15742-4
Online ISBN: 978-3-031-15743-1
eBook Packages: Computer ScienceComputer Science (R0)