  Anomaly detection has been considered under several extents of prior
knowledge. Unsupervised methods do not require any labelled data, whereas
semi-supervised methods leverage some known anomalies. Inspired by
mixture-of-experts models and the analysis of the hidden activations of neural
networks, we introduce a novel data-driven anomaly detection method called
ARGUE. Our method is not only applicable to unsupervised and semi-supervised
environments, but also profits from prior knowledge of self-supervised
settings. We designed ARGUE as a combination of dedicated expert networks,
which specialise on parts of the input data. For its final decision, ARGUE
fuses the distributed knowledge across the expert systems using a gated
mixture-of-experts architecture. Our evaluation motivates that prior knowledge
about the normal data distribution may be as valuable as known anomalies.
