mlsanityData to 14 Oct 2026

Anomaly Detection with Adversarial Dual Autoencoders

arXiv 1902.06924 ↗First posted 19 Feb 2019cs.CVcited by 16

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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.

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

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Citing papers per year: 2021: 7; 2022: 2; 2023: 3; 2024: 2; 2025: 0; 2026: 2.

16 papers.

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  1. 2021LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networkscited by 42
  2. 2021Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systemscited by 19
  3. 2022Machine Learning in NextG Networks via Generative Adversarial Networkscited by 8
  4. 2023Exploring the Potential of World Models for Anomaly Detection in Autonomous Drivingcited by 7
  5. 2024Latent space configuration for improved generalization in supervised autoencoder neural networkscited by 6
  6. 2021Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generatorscited by 3
  7. 2021PANDA : Perceptually Aware Neural Detection of Anomaliescited by 3
  8. 2021Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagerycited by 3
  9. 2024UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Drivingcited by 3
  10. 2021OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundarycited by 2
  11. 2022R2-AD2: Detecting Anomalies by Analysing the Raw Gradientcited by 2
  12. 2023Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruptioncited by 2
4 more
  1. 2023Conditioning Latent-Space Clusters for Real-World Anomaly Classificationcited by 1
  2. 2021New Perspective on Progressive GANs Distillation for One-class Novelty Detectioncited by 0
  3. 2026Convolutional Neural Network and Adversarial Autoencoder in EEG images classificationcited by 0
  4. 2026Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptationcited by 0