# Unsupervised Surrogate Anomaly Detection

arXiv: https://arxiv.org/abs/2504.20733 · HTML: https://mlsanity.com/paper/2504.20733/

## 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 (most cited first, 17 of 17)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [The Expressive Power of Neural Networks: A View from the Width](https://mlsanity.com/paper/1709.02540/) (2017)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Classification-Based Anomaly Detection for General Data](https://mlsanity.com/paper/2005.02359/) (2020)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [Neural Transformation Learning for Deep Anomaly Detection Beyond Images](https://mlsanity.com/paper/2103.16440/) (2021)
- [On Diffusion Modeling for Anomaly Detection](https://mlsanity.com/paper/2305.18593/) (2023)
- [A Survey on GANs for Anomaly Detection](https://mlsanity.com/paper/1906.11632/) (2019)
- [Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection](https://mlsanity.com/paper/1907.03813/) (2019)
- [Towards Fair Deep Anomaly Detection](https://mlsanity.com/paper/2012.14961/) (2020)
- [Meta-survey on outlier and anomaly detection](https://mlsanity.com/paper/2312.07101/) (2023)
- [Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework](https://mlsanity.com/paper/2012.04837/) (2020)

## Built on by (most cited first, 3 of 3)

- [FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models](https://mlsanity.com/paper/2603.17570/) (2026)
- [Unsupervised Symbolic Anomaly Detection](https://mlsanity.com/paper/2603.17575/) (2026)
- [On Uniformly Scaling Flows: A Density-Aligned Approach to Deep One-Class Classification](https://mlsanity.com/paper/2510.09452/) (2025)

## 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.