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Beyond Normal References: Discriminative Few-Shot Anomaly Detection

arXiv:2605.23231v2 Announce Type: replace Abstract: This paper considers a practical few-shot anomaly detection (FSAD) setting, termed discriminative FSAD, where a limited number of both normal and anomalous examples are available as references during inference. Existing FSAD methods rely on normal-only references through normality matching, ignoring the discriminative clues in anomalous references, while directly fitting both references can overfit to the seen anomalies. We introduce IDEAL,...

arXiv CS 8d ago