{
  "id": 3593,
  "url": "https://arxiv.org/abs/2605.28428v1",
  "title": "Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization",
  "summary": "Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anomaly detection is typically solved by measuring feature similarity of a query patch to a memory of normal patches. However, similarity alone does not reveal how strongly a query patch violates the structure of the normal feature manifold. We propose a training-free Laplacian graph energy optimization formulation, named ANoCo that scores Anomaly by th",
  "authors": "Jungwook Seo, Minjeong Kim, Younkwan Lee, Seungho Shin, Sungyong Baik",
  "category": "research",
  "topics": "environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-27T12:58:56.000Z",
  "fetched_at": "2026-07-14T16:30:23.245Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3593",
  "original_url": "https://arxiv.org/abs/2605.28428v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}