arXiv: cs.AI preprintsInternational6 November 2026
DART: Domain-Agnostic Residual Transfer for Generalist Anomaly Detection
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arXiv:2611.08271v1 Announce Type: cross Abstract: Generalist Anomaly Detection (GAD) aims to detect anomalies in unseen domains using only a few reference samples. Recent normal-abnormal guided methods improve this setting by exploiting abnormal references, but their residuals often mix transferable anomaly cues with domain-specific appearance factors such as texture, geometry, and acquisition conditions. This residual domain entanglement limits cross-domain transfer and may produce spurious activations on novel domains. We propose DART, a plug-in framework for normal-abnormal guided GAD that
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