用单一模型替代多模型，高效估算异常检测背景分布
SIGMA: Single Interpolated Generative Model for Anomalies
- 训练一个通用生成模型，通过参数插值推导信号区背景
- 计算成本降低，背景建模精度与灵敏度保持相近
- 适合大规模滑动窗口异常搜索场景
在共振异常检测中，准确建模每个信号区域的背景分布是关键。现有数据驱动方法如CATHODE需为每个信号区域在补集上单独训练生成模型，并插值到信号区。这种在多个信号区重复训练整个数据集的方法，在滑动窗口搜索中带来巨大计算开销。本文提出SIGMA，一种全新、完全数据驱动且计算高效的背景分布估计方法。其核心思想是仅用全部数据训练一个生成模型，再通过插值其参数至邻近的边带区域，从而获得信号区的背景模型。相比以往方法，SIGMA显著降低计算成本，同时保持相近的背景建模质量与对异常信号的敏感性。
原文摘要 · Abstract (English)
A key step in any resonant anomaly detection search is accurate modeling of the background distribution in each signal region. Data-driven methods like CATHODE accomplish this by training separate generative models on the complement of each signal region, and interpolating them into their corresponding signal regions. Having to re-train the generative model on essentially the entire dataset for each signal region is a major computational cost in a typical sliding window search with many signal regions. Here, we present SIGMA, a new, fully data-driven, computationally-efficient method for estimating background distributions. The idea is to train a single generative model on all of the data and interpolate its parameters in sideband regions in order to obtain a model for the background in the signal region. The SIGMA method significantly reduces the computational cost compared to previous approaches, while retaining a similar high quality of background modeling and sensitivity to anomalous signals.
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