mlsanityData to 14 Oct 2026

Unsupervised Surrogate Anomaly Detection

arXiv 2504.20733 ↗First posted 29 Apr 2025cs.LG cs.AIcited by 3

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Abstract (arXiv)

In this paper, we study unsupervised anomaly detection algorithms that learn a neural network representation, i.e. regular patterns of normal data, which anomalies are deviating from. Inspired by a similar concept in engineering, we refer to our methodology as surrogate anomaly detection. We formalize the concept of surrogate anomaly detection into a set of axioms required for optimal surrogate models and propose a new algorithm, named DEAN (Deep Ensemble ANomaly detection), designed to fulfill these criteria. We evaluate DEAN on 121 benchmark datasets, demonstrating its competitive performance against 19 existing methods, as well as the scalability and reliability of our method.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 56 references that are in the map. First posted on arXiv 29 Apr 2025.

Builds on

17 of 56 references.

References · 17

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  2. 2015Variational Inference with Normalizing Flowscited by 2,170
  3. 2018Deep Anomaly Detection with Outlier Exposurecited by 942
  4. 2014Deep metric learning using Triplet networkcited by 422
  5. 2017The Expressive Power of Neural Networks: A View from the Widthcited by 320
  6. 2019Deep Semi-Supervised Anomaly Detectioncited by 291
  7. 2022ADBench: Anomaly Detection Benchmarkcited by 232
  8. 2020Classification-Based Anomaly Detection for General Datacited by 211
  9. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  10. 2020COPOD: Copula-Based Outlier Detectioncited by 146
  11. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 99
  12. 2023On Diffusion Modeling for Anomaly Detectioncited by 72
5 more
  1. 2019A Survey on GANs for Anomaly Detectioncited by 32
  2. 2019Statistical Analysis of Nearest Neighbor Methods for Anomaly Detectioncited by 29
  3. 2020Towards Fair Deep Anomaly Detectioncited by 24
  4. 2023Meta-survey on outlier and anomaly detectioncited by 13
  5. 2020Deep Unsupervised Image Anomaly Detection: An Information Theoretic Frameworkcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026FoMo X: Modular Explainability Signals for Outlier Detection Foundation Modelscited by 2
  2. 2026Unsupervised Symbolic Anomaly Detectioncited by 1
  3. 2025On Uniformly Scaling Flows: A Density-Aligned Approach to Deep One-Class Classificationcited by 0