用少量异常样本微调自编码器，提升无监督异常检测能力
Strengthening Anomaly Awareness
- 先无监督训练，再用少量标签异常数据微调，增强异常重建误差
- 在多个数据集上实现更优的正常与异常样本分离效果
- 适合缺乏大量标注数据的异常检测场景
我们提出一种改进的异常感知框架，用于提升无监督异常检测性能。方法采用两阶段训练策略：模型首先在背景数据上无监督训练，随后使用少量标注异常样本进行微调，以促使异常样本产生更大的重构误差。我们在多种领域验证该方法，包括含合成异常的MNIST数据集、来自CICIDS基准的网络入侵数据、来自LHCO2020数据集的对撞机物理数据，以及标准模型有效场论（SMEFT）模拟事件。后者提供了希格斯玻色子产生中细微动量偏差的真实案例。所有情况下，模型均表现出对未见异常更高的敏感性，实现了正常与异常样本更优的分离。结果表明，仅通过有针对性的微调引入少量异常信息，即可显著提升无监督模型的泛化能力和检测性能。
原文摘要 · Abstract (English)
We present a refined version of the Anomaly Awareness framework for enhancing unsupervised anomaly detection. Our approach introduces minimal supervision into Variational Autoencoders (VAEs) through a two-stage training strategy: the model is first trained in an unsupervised manner on background data, and then fine-tuned using a small sample of labeled anomalies to encourage larger reconstruction errors for anomalous samples. We validate the method across diverse domains, including the MNIST dataset with synthetic anomalies, network intrusion data from the CICIDS benchmark, collider physics data from the LHCO2020 dataset, and simulated events from the Standard Model Effective Field Theory (SMEFT). The latter provides a realistic example of subtle kinematic deviations in Higgs boson production. In all cases, the model demonstrates improved sensitivity to unseen anomalies, achieving better separation between normal and anomalous samples. These results indicate that even limited anomaly information, when incorporated through targeted fine-tuning, can substantially improve the generalization and performance of unsupervised models for anomaly detection.
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