# Supervised Anomaly Detection based on Deep Autoregressive Density Estimators

arXiv: https://arxiv.org/abs/1904.06034 · HTML: https://mlsanity.com/paper/1904.06034/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

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

- [Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?](https://mlsanity.com/paper/2107.11587/) (2021)
- [Anomaly Detection and Generation with Diffusion Models: A Survey](https://mlsanity.com/paper/2506.09368/) (2025)
- [Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space](https://mlsanity.com/paper/2206.09543/) (2022)

## Abstract (arXiv)

We propose a supervised anomaly detection method based on neural density estimators, where the negative log likelihood is used for the anomaly score. Density estimators have been widely used for unsupervised anomaly detection. By the recent advance of deep learning, the density estimation performance has been greatly improved. However, the neural density estimators cannot exploit anomaly label information, which would be valuable for improving the anomaly detection performance. The proposed method effectively utilizes the anomaly label information by training the neural density estimator so that the likelihood of normal instances is maximized and the likelihood of anomalous instances is lower than that of the normal instances. We employ an autoregressive model for the neural density estimator, which enables us to calculate the likelihood exactly. With the experiments using 16 datasets, we demonstrate that the proposed method improves the anomaly detection performance with a few labeled anomalous instances, and achieves better performance than existing unsupervised and supervised anomaly detection methods.