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A Principled Approach to Unsupervised Anomaly Detection — arXiv:2609.21800

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21800
License (as archived): CC0-1.0
Archived: 2026-10-10T02:29:42.993Z
SHA-256 of the archived text: ac7524e630ef1f174934b2d37b8e2c802c1f4dd5928a5d81c9b551bdc09ae6e1
Stable URL of this page: https://metraipolis.com/documento/dc_0dfdba99dd09

How to cite

A Principled Approach to Unsupervised Anomaly Detection — arXiv:2609.21800 — arXiv (en), https://arxiv.org/abs/2609.21800 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0dfdba99dd09 (Metraipolis archive dc_0dfdba99dd09, sha256 ac7524e630ef1f174934b2d37b8e2c802c1f4dd5928a5d81c9b551bdc09ae6e1)

BibTeX

@misc{dc0dfdba99dd09,
  title = {A Principled Approach to Unsupervised Anomaly Detection — arXiv:2609.21800},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21800},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0dfdba99dd09; sha256 ac7524e630ef1f174934b2d37b8e2c802c1f4dd5928a5d81c9b551bdc09ae6e1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21800. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Traditional unsupervised anomaly detection (UAD) methods are designed to flag or localise deviations from a normative distribution, ignoring the underlying generative mechanisms of the anomalies. Yet the nature of an anomaly is often as important as its presence. We reformulate UAD as a Bayesian inverse problem, in which the objective is to infer the most probable corruption responsible for each observation. Our framework yields a probabilistic anomaly score as the energy of the inferred corruption parameters, and serves as a principled recipe for developing new UAD algorithms. We derive several existing methods as instances of the general framework, each corresponding to the same energy score under different modelling choices. Experimentally, we study the framework's components in a controlled setting, and improve object-class AUROC on the MVTec AD dataset by 2.3% by adapting the underlying corruption model. Finally, we validate the framework on a brain MRI benchmark, achieving strong detection performance while producing estimates of pathology intensity, bias, and geometry. Code is available at this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-21 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.21800 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T02:07:36.100Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).