# Universal Data Anomaly Detection via Inverse Generative Adversary Network

arXiv: https://arxiv.org/abs/2001.08809 · HTML: https://mlsanity.com/paper/2001.08809/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection](https://mlsanity.com/paper/2106.12382/) (2021)
- [Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos](https://mlsanity.com/paper/2202.06503/) (2022)
- [DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing](https://mlsanity.com/paper/2302.13877/) (2023)
- [An optimization method for out-of-distribution anomaly detection models](https://mlsanity.com/paper/2302.00939/) (2023)

## Abstract (arXiv)

The problem of detecting data anomaly is considered. Under the null hypothesis that models anomaly-free data, measurements are assumed to be from an unknown distribution with some authenticated historical samples. Under the composite alternative hypothesis, measurements are from an unknown distribution positive distance away from the distribution under the null hypothesis. No training data are available for the distribution of anomaly data. A semi-supervised deep learning technique based on an inverse generative adversary network is proposed.