How Many Categories Are Enough? Distribution-Free Certification Limits for Few-Shot Anomaly Thresholds
Read the original on arXiv Machine Learning →The paper investigates how many normal samples are required to reliably set an alarm threshold for few‑shot anomaly detectors, focusing on distribution‑free certification limits. Using a frozen DINOv2 PCA residual ranker on 15 MVTec and 12 VisA categories, the authors show that simple leave‑one‑image‑out calibration is limited by resolution and shift, leading to empirical false‑alarm rates far above the nominal level. They derive a category‑count feasibility calculus, demonstrating that at least 14, 29, and 59 independent category draws are needed for 95% upper confidence bounds at α=0.20, 0.10, and 0.05, and propose the CRESS protocol to split source categories into reference, proposal, and certification roles. whyItMatters":"The study provides concrete numerical thresholds for the amount of source evidence needed to guarantee reliable anomaly detection in new categories, informing practical deployment of few‑shot detectors."
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