Detecting Media Clones in Cultural Repositories Using a Positive Unlabeled Learning Approach

• URL: http://arxiv.org/abs/2604.04071v1
• Date: Sun, 05 Apr 2026 11:41:29 GMT
• Title: Detecting Media Clones in Cultural Repositories Using a Positive Unlabeled Learning Approach
• Abstract summary: We formulate curator-in-the-loop duplicate discovery in the AtticPOT repository as a Positive-Unlabeled (PU) learning problem.<n>We train a lightweight per-query Clone on augmented views of the anchor and score the unlabeled repository with an interpretable threshold on the latent l norm.<n>The system proposes candidates for curator verification, uncovering cross-record duplicates that were not verified a priori.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We formulate curator-in-the-loop duplicate discovery in the AtticPOT repository as a Positive-Unlabeled (PU) learning problem. Given a single anchor per artefact, we train a lightweight per-query Clone Encoder on augmented views of the anchor and score the unlabeled repository with an interpretable threshold on the latent l_2 norm. The system proposes candidates for curator verification, uncovering cross-record duplicates that were not verified a priori. On CIFAR-10 we obtain F1=96.37 (AUROC=97.97); on AtticPOT we reach F1=90.79 (AUROC=98.99), improving F1 by +7.70 points over the best baseline (SVDD) under the same lightweight backbone. Qualitative "find-similar" panels show stable neighbourhoods across viewpoint and condition. The method avoids explicit negatives, offers a transparent operating point, and fits de-duplication, record linkage, and curator-in-the-loop workflows.

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