# Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection

arXiv: https://arxiv.org/abs/1912.04418 · HTML: https://mlsanity.com/paper/1912.04418/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Cross-domain Robust Deepfake Bias Expansion Network for Face Forgery Detection](https://mlsanity.com/paper/2310.05124/) (2023)

## Abstract (arXiv)

Many real-world monitoring and surveillance applications require non-trivial anomaly detection to be run in the streaming model. We consider an incremental-learning approach, wherein a deep-autoencoding (DAE) model of what is normal is trained and used to detect anomalies at the same time. In the detection of anomalies, we utilise a novel thresholding mechanism, based on value at risk (VaR). We compare the resulting convolutional neural network (CNN) against a number of subspace methods, and present results on changedetection net.