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

ArXi:2605.28428v1 Announce Type: cross 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