Detecting Media Clones in Cultural Repositories Using a Positive Unlabeled Learning Approach
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 38 references that are in the map. First posted on arXiv 5 Apr 2026.
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References · 9
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
- 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
- 2017Positive-Unlabeled Learning with Non-Negative Risk Estimatorcited by 217
- 2018How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrievalcited by 73
- 2020A Decade Survey of Content Based Image Retrieval using Deep Learningcited by 72
- 2017OmniArt: Multi-task Deep Learning for Artistic Data Analysiscited by 16
- 2023Dens-PU: PU Learning with Density-Based Positive Labeled Augmentationcited by 4
- 2026ART3mis: Ray-Based Textual Annotation on 3D Cultural Objectscited by 1
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