mlsanityData to 14 Oct 2026

Variational Autoencoder for Anomaly Detection: A Comparative Study

arXiv 2408.13561 ↗First posted 24 Aug 2024cs.CV eess.IVcited by 6

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Abstract (arXiv)

This paper aims to conduct a comparative analysis of contemporary Variational Autoencoder (VAE) architectures employed in anomaly detection, elucidating their performance and behavioral characteristics within this specific task. The architectural configurations under consideration encompass the original VAE baseline, the VAE with a Gaussian Random Field prior (VAE-GRF), and the VAE incorporating a vision transformer (ViT-VAE). The findings reveal that ViT-VAE exhibits exemplary performance across various scenarios, whereas VAE-GRF may necessitate more intricate hyperparameter tuning to attain its optimal performance state. Additionally, to mitigate the propensity for over-reliance on results derived from the widely used MVTec dataset, this paper leverages the recently-public MiAD dataset for benchmarking. This deliberate inclusion seeks to enhance result competitiveness by alleviating the impact of domain-specific models tailored exclusively for MVTec, thereby contributing to a more robust evaluation framework. Codes is available at https://github.com/endtheme123/VAE-compare.git.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 24 Aug 2024.

Builds on

11 of 21 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  4. 2023Semantic Segmentation using Vision Transformers: A surveycited by 73
  5. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  6. 2022Self-Supervised Masking for Unsupervised Anomaly Detection and Localizationcited by 43
  7. 2022AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decodercited by 29
  8. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  9. 2022MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detectioncited by 19
  10. 2023Masked Autoencoders for Unsupervised Anomaly Detection in Medical Imagescited by 9
  11. 2023Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencodercited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2025Bounding Distributional Shifts in World Modeling through Novelty Detectioncited by 2
  2. 2026Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applicationscited by 1
  3. 2025Online Conformal Anomaly Detection with Prediction-Powered Data Acquisitioncited by 0
  4. 2025CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detectioncited by 0
  5. 2025Anomaly-Aware YOLO: A Frugal yet Robust Approach to Infrared Small Target Detectioncited by 0
  6. 2025Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approachcited by 0