# Anomaly scores for generative models

arXiv: https://arxiv.org/abs/1905.11890 · HTML: https://mlsanity.com/paper/1905.11890/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

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

- [Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers](https://mlsanity.com/paper/2207.06055/) (2022)

## Abstract (arXiv)

Reconstruction error is a prevalent score used to identify anomalous samples when data are modeled by generative models, such as (variational) auto-encoders or generative adversarial networks. This score relies on the assumption that normal samples are located on a manifold and all anomalous samples are located outside. Since the manifold can be learned only where the training data lie, there are no guarantees how the reconstruction error behaves elsewhere and the score, therefore, seems to be ill-defined. This work defines an anomaly score that is theoretically compatible with generative models, and very natural for (variational) auto-encoders as they seem to be prevalent. The new score can be also used to select hyper-parameters and models. Finally, we explain why reconstruction error delivers good experimental results despite weak theoretical justification.