Foundation Models for Anomaly Detection: Vision and Challenges
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Abstract (arXiv)
As data continues to grow in volume and complexity across domains such as finance, manufacturing, and healthcare, effective anomaly detection is essential for identifying irregular patterns that may signal critical issues. Recently, foundation models (FMs) have emerged as a powerful tool for advancing anomaly detection. They have demonstrated unprecedented capabilities in enhancing anomaly identification, generating detailed data descriptions, and providing visual explanations. This survey presents the first comprehensive review of recent advancements in FM-based anomaly detection. We propose a novel taxonomy that classifies FMs into three categories based on their roles in anomaly detection tasks, i.e., as encoders, detectors, or interpreters. We provide a systematic analysis of state-of-the-art methods and discuss key challenges in leveraging FMs for improved anomaly detection. We also outline future research directions in this rapidly evolving field.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 48 of its 60 references that are in the map. First posted on arXiv 10 Feb 2025.
Builds on
48 of 60 references.
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36 more
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Citing papers per year: 2025: 1; 2026: 2.
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