Abstract

Image-to-image translation involves translating images in one domain into images in another domain, while keeping some aspects of the image consistent across the domains. Image translation models that keep the category of the image consistent can be useful for applications like domain adaptation. Generative models like variational autoencoders have the ability to extract latent factors of generation from an image. Based on generative models like variational autoencoders and generative adversarial networks, we develop a semi-supervised image-to-image translation procedure. We apply this procedure to perform image translation and domain adaptation for complex digit datasets.

Original languageEnglish (US)
Title of host publicationSmart Multimedia - 1st International Conference, ICSM 2018, Revised Selected Papers
EditorsStefano Berretti, Anup Basu
PublisherSpringer Verlag
Pages334-344
Number of pages11
ISBN (Print)9783030043742
DOIs
StatePublished - Jan 1 2018
Event1st International Conference on Smart Multimedia, ICSM 2018 - Toulon, France
Duration: Aug 24 2018Aug 26 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11010 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference on Smart Multimedia, ICSM 2018
CountryFrance
CityToulon
Period8/24/188/26/18

Keywords

  • Domain adaptation
  • Generative adversarial networks
  • Image-to-image translation
  • Variational autoencoders

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Eusebio, J., Demakethepalli Venkateswara, H., & Panchanathan, S. (2018). Semi-supervised adversarial image-to-image translation. In S. Berretti, & A. Basu (Eds.), Smart Multimedia - 1st International Conference, ICSM 2018, Revised Selected Papers (pp. 334-344). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11010 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-04375-9_28