mlsanityData to 7 Oct 2026

TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models

arXiv 2307.12336 ↗First posted 23 Jul 2023cs.LGcited by 5

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Tables are an abundant form of data with use cases across all scientific fields. Real-world datasets often contain anomalous samples that can negatively affect downstream analysis. In this work, we only assume access to contaminated data and present a diffusion-based probabilistic model effective for unsupervised anomaly detection. Our model is trained to learn the density of normal samples by utilizing a unique rejection scheme to attenuate the influence of anomalies on the density estimation. At inference, we identify anomalies as samples in low-density regions. We use real data to demonstrate that our method improves detection capabilities over baselines. Furthermore, our method is relatively stable to the dimension of the data and does not require extensive hyperparameter tuning.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 25 of its 49 references that are in the map. First posted on arXiv 23 Jul 2023.

Builds on

25 of 49 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Exploring Diffusion Models for Unsupervised Video Anomaly Detection12 Apr

Other references · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  5. 2022Video Diffusion Modelscited by 2,280
  6. 2022eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoiserscited by 921
  7. 2021Revisiting Deep Learning Models for Tabular Datacited by 725
  8. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 620
  9. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  10. 2021Label-Efficient Semantic Segmentation with Diffusion Modelscited by 495
  11. 2022Diffusion Models for Medical Anomaly Detectioncited by 231
  12. 2022ADBench: Anomaly Detection Benchmarkcited by 230
12 more
  1. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
  2. 2020Classification-Based Anomaly Detection for General Datacited by 210
  3. 2021Diffusion Models for Implicit Image Segmentation Ensemblescited by 198
  4. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  5. 2020COPOD: Copula-Based Outlier Detectioncited by 143
  6. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 98
  7. 2018Generative Adversarial Active Learning for Unsupervised Outlier Detectioncited by 65
  8. 2018Anomaly Detection with Generative Adversarial Networks for Multivariate Time Seriescited by 55
  9. 2022Latent Outlier Exposure for Anomaly Detection with Contaminated Datacited by 48
  10. 2016Anomaly detection in online social networkscited by 15
  11. 2023Anomaly Detection with Variance Stabilized Density Estimationcited by 8
  12. 2021Probabilistic Robust Autoencoders for Outlier Detectioncited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Diffusion and Flow Matching Models for Tabular Data: A Surveycited by 8
  2. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  3. 2025Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Datacited by 3
  4. 2025RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentationcited by 3
  5. 2026Witnesses Explain Anomaliescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Anomaly Detection of Tabular Data Using LLMs
  2. 2025Research on Anomaly Detection Methods Based on Diffusion Models
  3. 2024Retrieval Augmented Deep Anomaly Detection for Tabular Data
  4. 2026Towards One-for-All Anomaly Detection for Tabular Data
  5. 2024Anomaly detection using Diffusion-based methods
  6. 2026Masked Diffusion Modeling for Anomaly Detection
  7. 2024TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation
  8. 2024DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector