用八种信号融合检测未知异常，比单信号方法更可靠。
Beyond a Single Signal: SPECTREG2, A Unified MultiExpert Anomaly Detector for Unknown Unknowns
- 多信号融合：从双主干网络提取8种互补异常信号
- 在多个数据集上优于基线，尤其擅长发现新变量和混杂因子
- 适合开放世界中需要安全决策的AI系统
认知智能要求机器学习系统能识别自身知识的边界，并在不确定性下安全行动，尤其面对未知未知时。现有不确定性量化方法依赖单一信号（如置信度或密度），难以检测多样结构异常。我们提出SPECTRE-G2，一种基于双主干神经网络的多信号异常检测器，融合八种互补信号：密度、几何、不确定性、判别性与因果信号。模型包含谱归一化高斯编码器、保留特征几何的MLP及五个集成模型。各信号通过验证集统计量归一化，并用合成分布外数据校准。自适应top-k融合选择最有效信号并平均得分。在合成数据、Adult、CIFAR-10和Gridworld数据集上的实验表明，其在多种异常类型下表现优异，于AUROC、AUPR和FPR95指标上超越多个基线。模型在不同随机种子下稳定，对新变量和混杂因子尤为有效。SPECTRE-G2为开放世界中未知未知检测提供了实用方案。
原文摘要 · Abstract (English)
Epistemic intelligence requires machine learning systems to recognise the limits of their own knowledge and act safely under uncertainty, especially when faced with unknown unknowns. Existing uncertainty quantification methods rely on a single signal such as confidence or density and fail to detect diverse structural anomalies. We introduce SPECTRE-G2, a multi-signal anomaly detector that combines eight complementary signals from a dual-backbone neural network. The architecture includes a spectral normalised Gaussianization encoder, a plain MLP preserving feature geometry, and an ensemble of five models. These produce density, geometry, uncertainty, discriminative, and causal signals. Each signal is normalised using validation statistics and calibrated with synthetic out-of-distribution data. An adaptive top-k fusion selects the most informative signals and averages their scores. Experiments on synthetic, Adult, CIFAR-10, and Gridworld datasets show strong performance across diverse anomaly types, outperforming multiple baselines on AUROC, AUPR, and FPR95. The model is stable across seeds and particularly effective for detecting new variables and confounders. SPECTRE-G2 provides a practical approach for detecting unknown unknowns in open-world settings.
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