通过检索增强实现异常检测模型实时优化，无需重新训练。
Retrieval Augmented Anomaly Detection (RAAD): Nimble Model Adjustment Without Retraining
- 利用人类标注样本构建向量库，动态修正模型输出。
- 在图像、文本、图数据上均实现高精度与低误报率。
- 适合对吞吐量要求高且需快速响应的工业级异常检测场景。
我们提出一种新型机制，用于实时（人机协同）反馈以减少异常检测中的误报，特别适用于轻量化行为网络异常检测模型的部署。该方法借鉴检索增强生成思想，将人工标注的示例存入向量存储，可在下一处理批次中即时调整模型推理结果。为验证该技术的通用性，我们在多种模型架构和多模态数据（包括图像、文本、图结构数据）上进行了测试，证明其在保持高精度的同时显著降低误报率。
原文摘要 · Abstract (English)
We propose a novel mechanism for real-time (human-in-the-loop) feedback focused on false positive reduction to enhance anomaly detection models. It was designed for the lightweight deployment of a behavioral network anomaly detection model. This methodology is easily integrable to similar domains that require a premium on throughput while maintaining high precision. In this paper, we introduce Retrieval Augmented Anomaly Detection, a novel method taking inspiration from Retrieval Augmented Generation. Human annotated examples are sent to a vector store, which can modify model outputs on the very next processed batch for model inference. To demonstrate the generalization of this technique, we benchmarked several different model architectures and multiple data modalities, including images, text, and graph-based data.
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