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Active Learning Autoencoder Anomaly Detection

Active Learning Autoencoder Anomaly Detection combines an autoencoder's unsupervised reconstruction-error scoring with an active learning query loop. The model flags high-error instances as candidate anomalies, selectively asks a human oracle to label the most informative ones, and iteratively retrains — achieving strong anomaly detection with only a small labeling budget.

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Sources

  1. Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249. DOI: 10.1016/j.sigpro.2013.12.026 ↗
  2. Zhu, Y., Lukasiewicz, T. (2020). DPLAN: Discourse-level Plan-based Text Generation. Proceedings of the 28th International Conference on Computational Linguistics, 3464–3474. (See also: Guo et al. (2018). Deep Active Learning for Anomaly Detection. Neurocomputing, 290, 135–143.) link ↗

Related methods

Active learning Isolation forestActive learning One-class SVMAutoencoder Anomaly DetectionBayesian Autoencoder Anomaly DetectionEnsemble Autoencoder Anomaly DetectionSemi-supervised Autoencoder Anomaly Detection

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ScholarGate — Active Learning Autoencoder Anomaly Detection (Active Learning-Guided Autoencoder Anomaly Detection). Retrieved 2026-06-04 from https://scholargate.app/en/machine-learning/active-learning-autoencoder-anomaly-detection