mlsanityData to 7 Oct 2026

Anomaly Detection With Conditional Variational Autoencoders

arXiv 2010.05531 ↗First posted 12 Oct 2020cs.LG hep-excited by 46

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Abstract (arXiv)

Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Previous works argued that training VAE models only with inliers is insufficient and the framework should be significantly modified in order to discriminate the anomalous instances. In this work, we exploit the deep conditional variational autoencoder (CVAE) and we define an original loss function together with a metric that targets hierarchically structured data AD. Our motivating application is a real world problem: monitoring the trigger system which is a basic component of many particle physics experiments at the CERN Large Hadron Collider (LHC). In the experiments we show the superior performance of this method for classical machine learning (ML) benchmarks and for our application.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Oct 2020.

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Citing papers per year: 2021: 17; 2022: 8; 2023: 6; 2024: 8; 2025: 6; 2026: 1.

46 papers.

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  1. 2022DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latentscited by 103
  2. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  3. 2021Anomaly Detection via Self-organizing Mapcited by 18
  4. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  5. 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
  6. 2021Online-compatible Unsupervised Non-resonant Anomaly Detectioncited by 15
  7. 2022Lorentz group equivariant autoencoderscited by 15
  8. 2021Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detectioncited by 13
  9. 2021An Introduction to Variational Inferencecited by 11
  10. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  11. 2023Anomalies, Representations, and Self-Supervisioncited by 10
  12. 2021CVAD: A generic medical anomaly detector based on Cascade VAEcited by 6
34 more
  1. 2024Paired Autoencoders for Likelihood-free Estimation in Inverse Problemscited by 6
  2. 2021Assessing the Quality of the Datasets by Identifying Mislabeled Samplescited by 5
  3. 2021Neural network based order parameter for phase transitions and its applications in high-entropy alloyscited by 4
  4. 2023Robust Errant Beam Prognostics with Conditional Modeling for Particle Acceleratorscited by 4
  5. 2023Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Paymentscited by 4
  6. 2021Anomaly Detection By Autoencoder Based On Weighted Frequency Domain Losscited by 3
  7. 2022Novel Applications for VAE-based Anomaly Detection Systemscited by 3
  8. 2023Generative Invertible Quantum Neural Networkscited by 3
  9. 2021Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distancecited by 2
  10. 2021Margin-Aware Intra-Class Novelty Identification for Medical Imagescited by 2
  11. 2024Unsupervised Multiple Domain Translation through Controlled Disentanglement in Variational Autoencodercited by 2
  12. 2021Generation Drawing/Grinding Trajectoy Based on Hierarchical CVAEcited by 1
  13. 2021Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEscited by 1
  14. 2022VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surfacecited by 1
  15. 2022Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attentioncited by 1
  16. 2022LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detectioncited by 1
  17. 2024A Survey on the Integration of Generative AI for Critical Thinking in Mobile Networkscited by 1
  18. 2024Improved Anomaly Detection through Conditional Latent Space VAE Ensemblescited by 1
  19. 2025Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attackscited by 1
  20. 2025Prior-Guided Residual Diffusion: Calibrated and Efficient Medical Image Segmentationcited by 1
  21. 2025Contextual Learning for Anomaly Detection in Tabular Datacited by 1
  22. 2021Scalable Microservice Forensics and Stability Assessment Using Variational Autoencoderscited by 0
  23. 2021Discriminating modelling approaches for Point in Time Economic Scenario Generationcited by 0
  24. 2021A Survey on Proactive Customer Care: Enabling Science and Steps to Realize itcited by 0
  25. 2022Collaborative Anomaly Detectioncited by 0
  26. 2023MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstructioncited by 0
  27. 2024Latent Anomaly Detection Through Density Matricescited by 0
  28. 2024Unsupervised Hybrid framework for ANomaly Detection (HAND) -- applied to Screening Mammogramcited by 0
  29. 2024AI-based particle track identification in scintillating fibres read out with imaging sensorscited by 0
  30. 2024Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distributioncited by 0
  31. 2025FL-CLEANER: byzantine and backdoor defense by CLustering Errors of Activation maps in Non-iid fedErated leaRningcited by 0
  32. 2025TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searchescited by 0
  33. 2025Video Anomaly Detection with Contours -- A Studycited by 0
  34. 2026Generative Bayesian Filtering for State Estimationcited by 0