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arXiv · 2008.13763

Anomaly Detection by Recombining Gated Unsupervised Experts

J. -P. Schulze·P. Sperl·K. Böttinger
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Abstract

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.

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J. -P. Schulze, P. Sperl, K. Böttinger. 2020-08-31. Anomaly Detection by Recombining Gated Unsupervised Experts. https://doi.org/10.1109/ijcnn55064.2022.9892807

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