# Anomaly Detection Based on Multiple-Hypothesis Autoencoder

arXiv: https://arxiv.org/abs/2107.08790 · HTML: https://mlsanity.com/paper/2107.08790/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Generative Probabilistic Novelty Detection with Adversarial Autoencoders](https://mlsanity.com/paper/1807.02588/) (2018)
- [Abnormal Event Detection in Videos using Spatiotemporal Autoencoder](https://mlsanity.com/paper/1701.01546/) (2017)
- [Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network](https://mlsanity.com/paper/1912.00583/) (2019)

## Abstract (arXiv)

Recently Autoencoder(AE) based models are widely used in the field of anomaly detection. A model trained with normal data generates a larger restoration error for abnormal data. Whether or not abnormal data is determined by observing the restoration error. It takes a lot of cost and time to obtain abnormal data in the industrial field. Therefore the model trains only normal data and detects abnormal data in the inference phase. However, the restoration area for the input data of AE is limited in the latent space. To solve this problem, we propose Multiple-hypothesis Autoencoder(MH-AE) model composed of several decoders. MH-AE model increases the restoration area through contention between decoders. The proposed method shows that the anomaly detection performance is improved compared to the traditional AE for various input datasets.