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Studying bias in visual features through the lens of optimal transport

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autorschaft

  • Simone Fabbrizzi
  • Xuan Zhao
  • Emmanouil Krasanakis
  • Symeon Papadopoulos
  • Eirini Ntoutsi

Organisationseinheiten

Externe Organisationen

  • Center For Research And Technology - Hellas
  • SCHUFA Holding AG
  • Eberhard Karls Universität Tübingen
  • Universität der Bundeswehr München

Details

OriginalspracheEnglisch
Seiten (von - bis)281–312
Seitenumfang32
FachzeitschriftData Mining and Knowledge Discovery
Jahrgang38
Frühes Online-Datum2 Sept. 2023
PublikationsstatusVeröffentlicht - Jan. 2024

Abstract

In this article the statement after Equation 1 had an error in the published version. Please refer the correction as follows: “where ν = T#µ and T# is the push-forward of µ along the function T : X → Y” was incorrectly written as “where T# is the push-forward of µ along the function T : X → Y. Furthermore, Equation 1 itself was incorrectly formulated. Namely, the integral should have been over X and not over X × Y. The original article has been corrected.

ASJC Scopus Sachgebiete

Zitieren

Studying bias in visual features through the lens of optimal transport. / Fabbrizzi, Simone; Zhao, Xuan; Krasanakis, Emmanouil et al.
in: Data Mining and Knowledge Discovery, Jahrgang 38, 01.2024, S. 281–312.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Fabbrizzi S, Zhao X, Krasanakis E, Papadopoulos S, Ntoutsi E. Studying bias in visual features through the lens of optimal transport. Data Mining and Knowledge Discovery. 2024 Jan;38:281–312. Epub 2023 Sep 2. doi: 10.1007/s10618-023-00972-2, 10.1007/s10618-023-00986-w
Fabbrizzi, Simone ; Zhao, Xuan ; Krasanakis, Emmanouil et al. / Studying bias in visual features through the lens of optimal transport. in: Data Mining and Knowledge Discovery. 2024 ; Jahrgang 38. S. 281–312.
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