# OneFlow: One-class flow for anomaly detection based on a minimal volume region

arXiv: https://arxiv.org/abs/2010.03002 · HTML: https://mlsanity.com/paper/2010.03002/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [A Survey of Visual Sensory Anomaly Detection](https://mlsanity.com/paper/2202.07006/) (2022)
- [TACTIC for Navigating the Unknown: Tabular Anomaly deteCTion via In-Context inference](https://mlsanity.com/paper/2603.14171/) (2026)
- [Few-Shot Anomaly Detection via Category-Agnostic Registration Learning](https://mlsanity.com/paper/2406.08810/) (2024)
- [Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination](https://mlsanity.com/paper/2110.14825/) (2021)
- [An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination](https://mlsanity.com/paper/2309.09436/) (2023)
- [VeriFlow: Modeling Distributions for Neural Network Verification](https://mlsanity.com/paper/2406.14265/) (2024)
- [On Uniformly Scaling Flows: A Density-Aligned Approach to Deep One-Class Classification](https://mlsanity.com/paper/2510.09452/) (2025)
- [Unsupervised Anomaly Detection Using Flow Matching on Tabular Data](https://mlsanity.com/paper/2608.19801/) (2026)
- [TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Tasks](https://mlsanity.com/paper/2609.36968/) (2026)

## Abstract (arXiv)

We propose OneFlow - a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region. Contrary to density-based methods, OneFlow is constructed in such a way that its result typically does not depend on the structure of outliers. This is caused by the fact that during training the gradient of the cost function is propagated only over the points located near to the decision boundary (behavior similar to the support vectors in SVM). The combination of flow models and a Bernstein quantile estimator allows OneFlow to find a parametric form of bounding region, which can be useful in various applications including describing shapes from 3D point clouds. Experiments show that the proposed model outperforms related methods on real-world anomaly detection problems.