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Semi-supervised Autoencoder Anomaly Detection

Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist.

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Sources

  1. Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020). link ↗
  2. Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., & Chen, H. (2018). Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2018). link ↗

Related methods

Autoencoder Anomaly DetectionIsolation ForestOne-class SVMSemi-supervised LearningSemi-supervised One-class SVM

Referenced by

Active Learning Autoencoder Anomaly DetectionBayesian Autoencoder Anomaly DetectionEnsemble Autoencoder Anomaly DetectionOnline Autoencoder Anomaly DetectionSelf-supervised Autoencoder Anomaly Detection

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ScholarGate — Semi-supervised Autoencoder Anomaly Detection (Semi-supervised Autoencoder-based Anomaly Detection). Retrieved 2026-06-04 from https://scholargate.app/tr/machine-learning/semi-supervised-autoencoder-anomaly-detection