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.
@article{arxiv.2604.04071,
title = {Detecting Media Clones in Cultural Repositories Using a Positive Unlabeled Learning Approach},
author = {V. Sevetlidis and V. Arampatzakis and M. Karta and I. Mourthos and D. Tsiafaki and G. Pavlidis},
journal= {arXiv preprint arXiv:2604.04071},
year = {2026}
}