SOTAVerified

Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

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Showing 2351–2375 of 4856 papers

TitleStatusHype
Algorithmic Recourse for Anomaly Detection in Multivariate Time Series—0
Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical SystemsCode0
Human Kinematics-inspired Skeleton-based Video Anomaly DetectionCode0
Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New ApproachCode0
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets—0
(Predictable) Performance Bias in Unsupervised Anomaly Detection—0
Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence—0
Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challenges—0
STemGAN: spatio-temporal generative adversarial network for video anomaly detection—0
mixed attention auto encoder for multi-class industrial anomaly detection—0
Ano-SuPs: Multi-size anomaly detection for manufactured products by identifying suspected patches—0
Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing—0
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward—0
Semi-automatic staging area for high-quality structured data extraction from scientific literatureCode0
Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter—0
Active anomaly detection based on deep one-class classification—0
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination—0
Context-Aware Deep Time-Series Decomposition for Anomaly Detection in BusinessesCode0
Forensic Video Analytic Software—0
Enhancing Visual Perception in Novel Environments via Incremental Data Augmentation Based on Style TransferCode0
Understanding the limitations of self-supervised learning for tabular anomaly detection—0
An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone—0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
Beta quantile regression for robust estimation of uncertainty in the presence of outliers—0
Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified