Beyond Normal References: Discriminative Few-Shot Anomaly Detection

Name: Beyond Normal References: Discriminative Few-Shot Anomaly Detection
Uploaded: 2026-10-02T04:24:19.315596+00:00
Duration: 868 s
Description: IDEAL is a learning framework for few-shot anomaly detection that uses both normal and abnormal examples to find generalizable abnormality patterns. It addresses limitations by characterizing abnormality as deviations from normality, allowing it to detect unseen anomalies effectively even when only a few references are available during testing.

IDEAL is a learning framework for few-shot anomaly detection that uses both normal and abnormal examples to find generalizable abnormality patterns. It addresses limitations by characterizing abnormality as deviations from normality, allowing it to detect unseen anomalies effectively even when only a few references are available during testing.

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