Existing works on anomaly detection (AD) rely on clean labels from human
annotators that are expensive to acquire in practice. In this work, we propose
a method to leverage weak/noisy labels (e.g., risk scores generated by machine
rules for detecting malware) that are cheaper to obtain for anomaly detection.
Specifically, we propose ADMoE, the first framework for anomaly detection
algorithms to learn from noisy labels. In a nutshell, ADMoE leverages
mixture-of-experts (MoE) architecture to encourage specialized and scalable
learning from multiple noisy sources. It captures the similarities among noisy
labels by sharing most model parameters, while encouraging specialization by
building "expert" sub-networks. To further juice out the signals from noisy
labels, ADMoE uses them as input features to facilitate expert learning.
Extensive results on eight datasets (including a proprietary enterprise
security dataset) demonstrate the effectiveness of ADMoE, where it brings up to
34% performance improvement over not using it. Also, it outperforms a total of
13 leading baselines with equivalent network parameters and FLOPS. Notably,
ADMoE is model-agnostic to enable any neural network-based detection methods to
handle noisy labels, where we showcase its results on both multiple-layer
perceptron (MLP) and the leading AD method DeepSAD.

提出了一种用于从较便宜的弱 / 噪声标签中学习的 ADMoE 方法，该方法通过 MoE 构架鼓励专业和可扩展的学习，并通过构建 “专家” 子网络来鼓励专业化，在 8 个数据集中有效地使用，可以提高 34％的性能。