# Anomaly Detection With Conditional Variational Autoencoders

arXiv: https://arxiv.org/abs/2010.05531 · HTML: https://mlsanity.com/paper/2010.05531/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 30 of 46)

- [DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents](https://mlsanity.com/paper/2201.00308/) (2022)
- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Anomaly Detection via Self-organizing Map](https://mlsanity.com/paper/2107.09903/) (2021)
- [Deep Generative Models for Detector Signature Simulation: A Taxonomic Review](https://mlsanity.com/paper/2312.09597/) (2023)
- [Challenges for Unsupervised Anomaly Detection in Particle Physics](https://mlsanity.com/paper/2110.06948/) (2021)
- [Online-compatible Unsupervised Non-resonant Anomaly Detection](https://mlsanity.com/paper/2111.06417/) (2021)
- [Lorentz group equivariant autoencoders](https://mlsanity.com/paper/2212.07347/) (2022)
- [Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection](https://mlsanity.com/paper/2104.02092/) (2021)
- [An Introduction to Variational Inference](https://mlsanity.com/paper/2108.13083/) (2021)
- [Anomalies, Representations, and Self-Supervision](https://mlsanity.com/paper/2301.04660/) (2023)
- [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://mlsanity.com/paper/2209.07559/) (2022)
- [CVAD: A generic medical anomaly detector based on Cascade VAE](https://mlsanity.com/paper/2110.15811/) (2021)
- [Paired Autoencoders for Likelihood-free Estimation in Inverse Problems](https://mlsanity.com/paper/2405.13220/) (2024)
- [Assessing the Quality of the Datasets by Identifying Mislabeled Samples](https://mlsanity.com/paper/2109.05000/) (2021)
- [Neural network based order parameter for phase transitions and its applications in high-entropy alloys](https://mlsanity.com/paper/2109.05598/) (2021)
- [Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators](https://mlsanity.com/paper/2312.10040/) (2023)
- [Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments](https://mlsanity.com/paper/2312.13896/) (2023)
- [Anomaly Detection By Autoencoder Based On Weighted Frequency Domain Loss](https://mlsanity.com/paper/2105.10214/) (2021)
- [Novel Applications for VAE-based Anomaly Detection Systems](https://mlsanity.com/paper/2204.12577/) (2022)
- [Generative Invertible Quantum Neural Networks](https://mlsanity.com/paper/2302.12906/) (2023)
- [Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distance](https://mlsanity.com/paper/2104.06193/) (2021)
- [Margin-Aware Intra-Class Novelty Identification for Medical Images](https://mlsanity.com/paper/2108.00117/) (2021)
- [Unsupervised Multiple Domain Translation through Controlled Disentanglement in Variational Autoencoder](https://mlsanity.com/paper/2401.09180/) (2024)
- [Generation Drawing/Grinding Trajectoy Based on Hierarchical CVAE](https://mlsanity.com/paper/2111.10954/) (2021)
- [Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs](https://mlsanity.com/paper/2112.01221/) (2021)
- [VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surface](https://mlsanity.com/paper/2203.01193/) (2022)
- [Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention](https://mlsanity.com/paper/2206.07519/) (2022)
- [LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection](https://mlsanity.com/paper/2211.08525/) (2022)
- [A Survey on the Integration of Generative AI for Critical Thinking in Mobile Networks](https://mlsanity.com/paper/2404.06946/) (2024)
- [Improved Anomaly Detection through Conditional Latent Space VAE Ensembles](https://mlsanity.com/paper/2410.12328/) (2024)

## 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.