mlsanityData to 14 Oct 2026

ADBench: Anomaly Detection Benchmark

arXiv 2206.09426 ↗First posted 19 Jun 2022cs.LG cs.AIcited by 232

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Abstract (arXiv)

Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most comprehensive anomaly detection benchmark with 30 algorithms on 57 benchmark datasets, named ADBench. Our extensive experiments (98,436 in total) identify meaningful insights into the role of supervision and anomaly types, and unlock future directions for researchers in algorithm selection and design. With ADBench, researchers can easily conduct comprehensive and fair evaluations for newly proposed methods on the datasets (including our contributed ones from natural language and computer vision domains) against the existing baselines. To foster accessibility and reproducibility, we fully open-source ADBench and the corresponding results.

Key facts. Cited by 232 papers in the map (data through 14 Oct 2026). Builds on 94 of its 203 references that are in the map. First posted on arXiv 19 Jun 2022.

Builds on

94 of 203 references.

References · 94

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  6. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  7. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  8. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  9. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  10. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  11. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  12. 2020Energy-based Out-of-distribution Detectioncited by 1,299
38 more
  1. 2016An overview of gradient descent optimization algorithmscited by 1,033
  2. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  3. 2021Revisiting Deep Learning Models for Tabular Datacited by 740
  4. 2018Deep learning for time series classification: a reviewcited by 576
  5. 2021Deep Neural Networks and Tabular Data: A Surveycited by 491
  6. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  7. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  8. 2018Recent Advances in Open Set Recognition: A Surveycited by 310
  9. 2019Deep Semi-Supervised Anomaly Detectioncited by 291
  10. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 264
  11. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  12. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 250
  13. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  14. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 222
  15. 2020Classification-Based Anomaly Detection for General Datacited by 211
  16. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  17. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  18. 2020PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentationcited by 175
  19. 2019SELF: Learning to Filter Noisy Labels with Self-Ensemblingcited by 164
  20. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  21. 2019Deep Anomaly Detection with Deviation Networkscited by 153
  22. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  23. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  24. 2020COPOD: Copula-Based Outlier Detectioncited by 146
  25. 2020Adversarial Self-Supervised Contrastive Learningcited by 139
  26. 2019MNIST-C: A Robustness Benchmark for Computer Visioncited by 138
  27. 2020Explainable Deep One-Class Classificationcited by 127
  28. 2021Learning Placeholders for Open-Set Recognitioncited by 121
  29. 2018Adversarially Learned Anomaly Detectioncited by 115
  30. 2019Meta Label Correction for Noisy Label Learningcited by 108
  31. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  32. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 99
  33. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 88
  34. 2018Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detectioncited by 79
  35. 2020Do Wider Neural Networks Really Help Adversarial Robustness?cited by 79
  36. 2021When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?cited by 77
  37. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
  38. 2014Toward Supervised Anomaly Detectioncited by 73

50 most cited of 94 All 94 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 17; 2023: 57; 2024: 51; 2025: 58; 2026: 49.

232 papers.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
  2. 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 481
  3. 2022OpenOOD: Benchmarking Generalized Out-of-Distribution Detectioncited by 268
  4. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 82
  5. 2022BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphscited by 80
  6. 2023On Diffusion Modeling for Anomaly Detectioncited by 72
  7. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  8. 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detectioncited by 58
  9. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  10. 2024SoftPatch: Unsupervised Anomaly Detection with Noisy Datacited by 55
  11. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  12. 2023ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognitioncited by 31
38 more
  1. 2024MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalitiescited by 28
  2. 2024MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objectscited by 26
  3. 2023Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learningcited by 25
  4. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  5. 2023Weakly Supervised Anomaly Detection: A Surveycited by 22
  6. 2023Unsupervised anomaly detection algorithms on real-world data: how many do we need?cited by 20
  7. 2022Toward Unsupervised Outlier Model Selectioncited by 19
  8. 2024Building AI Agents for Autonomous Clouds: Challenges and Design Principlescited by 19
  9. 2023Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approachcited by 18
  10. 2024Supervised Anomaly Detection for Complex Industrial Imagescited by 18
  11. 2024PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selectioncited by 17
  12. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 17
  13. 2023Beyond Individual Input for Deep Anomaly Detection on Tabular Datacited by 16
  14. 2022ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labelscited by 15
  15. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 15
  16. 2022Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detectioncited by 14
  17. 2023NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generationcited by 14
  18. 2023Deep Orthogonal Hypersphere Compression for Anomaly Detectioncited by 12
  19. 2022Adversarial Camouflage for Node Injection Attack on Graphscited by 11
  20. 2023DP-SGD Without Clipping: The Lipschitz Neural Network Waycited by 11
  21. 2023Improving Generalizability of Graph Anomaly Detection Models via Data Augmentationcited by 11
  22. 2023RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervisioncited by 11
  23. 2023ADGym: Design Choices for Deep Anomaly Detectioncited by 11
  24. 2023Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networkscited by 10
  25. 2024Weakly Supervised Anomaly Detection via Knowledge-Data Alignmentcited by 10
  26. 2024Anomaly Detection Based on Isolation Mechanisms: A Surveycited by 10
  27. 2026SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarioscited by 10
  28. 2022Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learningcited by 9
  29. 2023Unsupervised Anomaly Detection with Rejectioncited by 9
  30. 2023Visual Data-Type Understanding does not emerge from Scaling Vision-Language Modelscited by 9
  31. 2024Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarkingcited by 9
  32. 2024Exploring What Why and How: A Multifaceted Benchmark for Causation Understanding of Video Anomalycited by 9
  33. 2025ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Modelscited by 9
  34. 2025Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecastingcited by 9
  35. 2026From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detectioncited by 9
  36. 2022Anomaly Detection through Unsupervised Federated Learningcited by 8
  37. 2023Intrinsic Self-Supervision for Data Quality Auditscited by 8
  38. 2023Anomaly Detection with Variance Stabilized Density Estimationcited by 8

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