# Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models

arXiv: https://arxiv.org/abs/1904.00548 · HTML: https://mlsanity.com/paper/1904.00548/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

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

- [Zero-Shot Anomaly Detection via Batch Normalization](https://mlsanity.com/paper/2302.07849/) (2023)
- [Contextual Learning for Anomaly Detection in Tabular Data](https://mlsanity.com/paper/2509.09030/) (2025)
- [Context-Aware Autoencoders for Anomaly Detection in Maritime Surveillance](https://mlsanity.com/paper/2602.00124/) (2026)

## Abstract (arXiv)

A method for unsupervised contextual anomaly detection is proposed using a cross-linked pair of Variational Auto-Encoders for assigning a normality score to an observation. The method enables a distinct separation of contextual from behavioral attributes and is robust to the presence of anomalous or novel contextual attributes. The method can be trained with data sets that contain anomalies without any special pre-processing.