Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization

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

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ethics.ai (27 May 2026), “Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization,” evidence record 3593, https://ethics.ai/record/3593 (originally published by arXiv).

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