# Anomaly Detection with Adversarial Dual Autoencoders

arXiv: https://arxiv.org/abs/1902.06924 · HTML: https://mlsanity.com/paper/1902.06924/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2019.

## Built on by (most cited first, 16 of 16)

- [LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks](https://mlsanity.com/paper/2112.05355/) (2021)
- [Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems](https://mlsanity.com/paper/2108.11800/) (2021)
- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving](https://mlsanity.com/paper/2308.05701/) (2023)
- [Latent space configuration for improved generalization in supervised autoencoder neural networks](https://mlsanity.com/paper/2402.08441/) (2024)
- [Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators](https://mlsanity.com/paper/2101.04645/) (2021)
- [PANDA : Perceptually Aware Neural Detection of Anomalies](https://mlsanity.com/paper/2104.13702/) (2021)
- [Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery](https://mlsanity.com/paper/2112.00556/) (2021)
- [UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving](https://mlsanity.com/paper/2406.06370/) (2024)
- [OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary](https://mlsanity.com/paper/2110.15273/) (2021)
- [R2-AD2: Detecting Anomalies by Analysing the Raw Gradient](https://mlsanity.com/paper/2206.10259/) (2022)
- [Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption](https://mlsanity.com/paper/2303.03925/) (2023)
- [Conditioning Latent-Space Clusters for Real-World Anomaly Classification](https://mlsanity.com/paper/2309.09676/) (2023)
- [New Perspective on Progressive GANs Distillation for One-class Novelty Detection](https://mlsanity.com/paper/2109.07295/) (2021)
- [Convolutional Neural Network and Adversarial Autoencoder in EEG images classification](https://mlsanity.com/paper/2604.04313/) (2026)
- [Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation](https://mlsanity.com/paper/2604.14726/) (2026)

## Abstract (arXiv)

Semi-supervised and unsupervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently. However, GAN training is somewhat challenging and unstable. Inspired from previous work in GAN-based image generation, we introduce a GAN-based anomaly detection framework - Adversarial Dual Autoencoders (ADAE) - consists of two autoencoders as generator and discriminator to increase training stability. We also employ discriminator reconstruction error as anomaly score for better detection performance. Experiments across different datasets of varying complexity show strong evidence of a robust model that can be used in different scenarios, one of which is brain tumor detection.