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Unsupervised Latent Space Translation Network

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F20%3A00343432" target="_blank" >RIV/68407700:21240/20:00343432 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.esann.org/sites/default/files/proceedings/2020/ES2020-64.pdf" target="_blank" >https://www.esann.org/sites/default/files/proceedings/2020/ES2020-64.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Unsupervised Latent Space Translation Network

  • Original language description

    One task that is often discussed in a computer vision is the mapping of an image from one domain to a corresponding image in another domain known as image-to-image translation. Currently there are several approaches solving this task. In this paper, we present an enhancement of the UNIT framework that aids in removing its main drawbacks. More specifically, we introduce an additional adversarial discriminator on the latent representation used instead of VAE, which enforces the latent space distributions of both domains to be similar. On MNIST and USPS domain adaptation tasks, this approach greatly outperforms competing approaches.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    ESANN 2020 - Proceedings

  • ISBN

    978-2-87587-074-2

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    13-18

  • Publisher name

    Ciaco - i6doc.com

  • Place of publication

    Louvain la Neuve

  • Event location

    Bruges

  • Event date

    Oct 2, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article