# Reconstruction Error-based Anomaly Detection with Few Outlying Examples

arXiv: https://arxiv.org/abs/2305.10464 · HTML: https://mlsanity.com/paper/2305.10464/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 81 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 24 of 24)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Deep Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/1911.08623/) (2019)
- [Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2203.14506/) (2022)
- [Generative Cooperative Learning for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2203.03962/) (2022)
- [UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection](https://mlsanity.com/paper/2111.08644/) (2021)
- [A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video](https://mlsanity.com/paper/2008.12328/) (2020)
- [Toward Supervised Anomaly Detection](https://mlsanity.com/paper/1401.6424/) (2014)
- [Generative Adversarial Active Learning for Unsupervised Outlier Detection](https://mlsanity.com/paper/1809.10816/) (2018)
- [Robust Anomaly Detection in Images using Adversarial Autoencoders](https://mlsanity.com/paper/1901.06355/) (2019)
- [Learning Not to Reconstruct Anomalies](https://mlsanity.com/paper/2110.09742/) (2021)
- [DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection](https://mlsanity.com/paper/2106.05410/) (2021)
- [Unsupervised anomaly detection algorithms on real-world data: how many do we need?](https://mlsanity.com/paper/2305.00735/) (2023)
- [Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning](https://mlsanity.com/paper/1708.08985/) (2017)
- [IoT Anomaly Detection Methods and Applications: A Survey](https://mlsanity.com/paper/2207.09092/) (2022)
- [Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset](https://mlsanity.com/paper/2211.15513/) (2022)
- [CFOF: A Concentration Free Measure for Anomaly Detection](https://mlsanity.com/paper/1901.04992/) (2019)
- [A3: Activation Anomaly Analysis](https://mlsanity.com/paper/2003.01801/) (2020)

## Built on by (most cited first, 5 of 5)

- [Novelty Detection in Reinforcement Learning with World Models](https://mlsanity.com/paper/2310.08731/) (2023)
- [QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations](https://mlsanity.com/paper/2402.17516/) (2024)
- [Link-Aware Energy-Frugal Continual Learning for Fault Detection in IoT Networks](https://mlsanity.com/paper/2512.13340/) (2025)
- [Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC](https://mlsanity.com/paper/2512.24290/) (2025)
- [Variational Autoencoders for P-wave Detection on Strong Motion Earthquake Spectrograms](https://mlsanity.com/paper/2601.05759/) (2026)

## Abstract (arXiv)

Reconstruction error-based neural architectures constitute a classical deep learning approach to anomaly detection which has shown great performances. It consists in training an Autoencoder to reconstruct a set of examples deemed to represent the normality and then to point out as anomalies those data that show a sufficiently large reconstruction error. Unfortunately, these architectures often become able to well reconstruct also the anomalies in the data. This phenomenon is more evident when there are anomalies in the training set. In particular when these anomalies are labeled, a setting called semi-supervised, the best way to train Autoencoders is to ignore anomalies and minimize the reconstruction error on normal data. The goal of this work is to investigate approaches to allow reconstruction error-based architectures to instruct the model to put known anomalies outside of the domain description of the normal data. Specifically, our strategy exploits a limited number of anomalous examples to increase the contrast between the reconstruction error associated with normal examples and those associated with both known and unknown anomalies, thus enhancing anomaly detection performances. The experiments show that this new procedure achieves better performances than the standard Autoencoder approach and the main deep learning techniques for semi-supervised anomaly detection.