HUMAN-CENTRIC VISUAL DIVERSITY AUDITING

A methodology for auditing the visual diversity of unlabeled human face image datasets uses a set of core human interpretable dimensions derived from human similarity judgments. Given a face image, a model can output dimensional values aligned with the human mental representational space of faces, w...

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Bibliographic Details
Main Authors Andrews, Jerone, Xiang, Alice, Joniak, Przemyslaw Kamil
Format Patent
LanguageEnglish
Published 26.10.2023
Subjects
CALCULATING
COMPUTING
COUNTING
PHYSICS
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Summary:A methodology for auditing the visual diversity of unlabeled human face image datasets uses a set of core human interpretable dimensions derived from human similarity judgments. Given a face image, a model can output dimensional values aligned with the human mental representational space of faces, where values not only express the presence of a feature, but also its extent. Since the model can be learned entirely from human behavior, the learned dimensions are not biased toward features that are easier to verbalize or quantify.
Bibliography:Application Number: US202318302257