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

Showing 125 of 4856 papers

TitleStatusHype
Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled EnsembleCode9
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect DetectionCode9
TimesNet: Temporal 2D-Variation Modeling for General Time Series AnalysisCode6
VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic FaithfulnessCode5
MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly DetectionCode5
Reservoir-enhanced Segment Anything Model for Subsurface DiagnosisCode5
Long-term Forecasting with TiDE: Time-series Dense EncoderCode5
aeon: a Python toolkit for learning from time seriesCode5
TimeMixer++: A General Time Series Pattern Machine for Universal Predictive AnalysisCode5
Deep Industrial Image Anomaly Detection: A SurveyCode4
Are Transformers Effective for Time Series Forecasting?Code4
Transformers in Time Series: A SurveyCode4
UniTS: A Unified Multi-Task Time Series ModelCode4
A Survey on Diffusion Models for Time Series and Spatio-Temporal DataCode4
Timer: Generative Pre-trained Transformers Are Large Time Series ModelsCode4
Video-XL: Extra-Long Vision Language Model for Hour-Scale Video UnderstandingCode4
Hawk: Learning to Understand Open-World Video AnomaliesCode3
Greykite: Deploying Flexible Forecasting at Scale at LinkedInCode3
INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual LearningCode3
ADBench: Anomaly Detection BenchmarkCode3
AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language ModelsCode3
GluonTS: Probabilistic Time Series Models in PythonCode3
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time SeriesCode3
Large-Scale Intelligent MicroservicesCode3
Deep Learning for Trajectory Data Management and Mining: A Survey and BeyondCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified