ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels. (arXiv:2208.11290v2 [cs.LG] UPDATED)

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 …

anomaly detection detection