# Autoencoding Binary Classifiers for Supervised Anomaly Detection

arXiv: https://arxiv.org/abs/1903.10709 · HTML: https://mlsanity.com/paper/1903.10709/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2019.

## Built on by (most cited first, 17 of 17)

- [Learning Not to Reconstruct Anomalies](https://mlsanity.com/paper/2110.09742/) (2021)
- [Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection](https://mlsanity.com/paper/2110.09768/) (2021)
- [PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies](https://mlsanity.com/paper/2303.10704/) (2023)
- [Understanding the Effect of Bias in Deep Anomaly Detection](https://mlsanity.com/paper/2105.07346/) (2021)
- [Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies](https://mlsanity.com/paper/2405.05886/) (2024)
- [Deep Visual Anomaly detection with Negative Learning](https://mlsanity.com/paper/2105.11058/) (2021)
- [Novel Applications for VAE-based Anomaly Detection Systems](https://mlsanity.com/paper/2204.12577/) (2022)
- [Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection](https://mlsanity.com/paper/2312.01541/) (2023)
- [R2-AD2: Detecting Anomalies by Analysing the Raw Gradient](https://mlsanity.com/paper/2206.10259/) (2022)
- [Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets](https://mlsanity.com/paper/2403.15790/) (2024)
- [Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data](https://mlsanity.com/paper/2405.18929/) (2024)
- [Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation](https://mlsanity.com/paper/2503.03789/) (2025)
- [Fault Detection and Monitoring using a Data-Driven Information-Based Strategy: Method, Theory, and Application](https://mlsanity.com/paper/2405.03667/) (2024)
- [Phishing the Phishers with SpecularNet: Hierarchical Graph Autoencoding for Reference-Free Web Phishing Detection](https://mlsanity.com/paper/2603.01874/) (2026)
- [AGAD: Adversarial Generative Anomaly Detection](https://mlsanity.com/paper/2304.04211/) (2023)
- [An Autoencoder Architecture for L-band Passive Microwave Retrieval of Landscape Freeze-Thaw Cycle](https://mlsanity.com/paper/2407.04119/) (2024)
- [Rare anomalies require large datasets: About proving the existence of anomalies](https://mlsanity.com/paper/2508.09894/) (2025)

## Abstract (arXiv)

We propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervised and unsupervised. The supervised approach accurately detects the known anomalies included in training data, but it cannot detect the unknown anomalies. Meanwhile, the unsupervised approach can detect both known and unknown anomalies that are located away from normal data points. However, it does not detect known anomalies as accurately as the supervised approach. Furthermore, even if we have labeled normal data points and anomalies, the unsupervised approach cannot utilize these labels. The ABC is a probabilistic binary classifier that effectively exploits the label information, where normal data points are modeled using the AE as a component. By maximizing the likelihood, the AE in the proposed ABC is trained to minimize the reconstruction error for normal data points, and to maximize it for known anomalies. Since our approach becomes able to reconstruct the normal data points accurately and fails to reconstruct the known and unknown anomalies, it can accurately discriminate both known and unknown anomalies from normal data points. Experimental results show that the ABC achieves higher detection performance than existing supervised and unsupervised methods.