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 3251–3275 of 4856 papers

TitleStatusHype
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection—0
ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels—0
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection—0
AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework—0
ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency—0
ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems—0
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level—0
ADSaS: Comprehensive Real-time Anomaly Detection System—0
ADS-ME: Anomaly Detection System for Micro-expression Spotting—0
ADT: Agent-based Dynamic Thresholding for Anomaly Detection—0
ADTR: Anomaly Detection Transformer with Feature Reconstruction—0
A Dual-Path Framework with Frequency-and-Time Excited Network for Anomalous Sound Detection—0
Advances on the classification of radio image cubes—0
Advancing Video Anomaly Detection: A Concise Review and a New Dataset—0
Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs—0
Advancing climate model interpretability: Feature attribution for Arctic melt anomalies—0
Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data—0
Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approaches—0
ADVENT: Attack/Anomaly Detection in VANETs—0
Adversarial Activation Patching: A Framework for Detecting and Mitigating Emergent Deception in Safety-Aligned Transformers—0
Sequential Adversarial Anomaly Detection for One-Class Event Data—0
Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection—0
Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy—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