用大模型提升异常检测，分类清晰、前景明确。
Foundation Models for Anomaly Detection: Vision and Challenges
- 按编码器、检测器、解释器角色分类大模型应用
- 系统梳理当前主流方法与核心挑战
- 适合关注大模型在工业异常检测中应用者
随着金融、制造、医疗等领域数据量和复杂性持续增长，有效异常检测对识别可能预示重大问题的异常模式至关重要。近年来，基础模型（FMs）已成为推动异常检测发展的强大工具，展现出前所未有的能力，包括提升异常识别精度、生成详细数据描述以及提供可视化解释。本文首次全面综述了基于基础模型的异常检测最新进展。我们提出一种新分类法，根据基础模型在异常检测任务中的角色，将其分为编码器、检测器和解释器三类。系统分析了当前最先进的方法，并探讨了利用基础模型提升异常检测的关键挑战。同时，本文还展望了该快速演进领域未来的研究方向。
原文摘要 · Abstract (English)
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.
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