🤖 AI Summary
This study addresses the challenge of detecting unlabeled cross-record media duplicates in cultural heritage databases by formulating it as a positive-unlabeled (PU) learning task. The authors propose a lightweight method that requires no explicit negative samples: a query encoder is trained using only a single positive example, leveraging data augmentation and latent representation learning, while L2-norm thresholds in the embedding space transparently filter candidate clones. This approach represents the first effective application of PU learning to media deduplication in cultural heritage contexts. Evaluated on the AtticPOT dataset, it achieves an F1 score of 90.79 (AUROC = 98.99), outperforming the SVDD baseline by 7.70 F1 points; on CIFAR-10, it attains an F1 of 96.37, demonstrating robustness to viewpoint and condition variations. The method is well-suited for curator workflows involving duplicate detection and record linkage.
📝 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.