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 76–100 of 4856 papers

TitleStatusHype
Tests for model misspecification in simulation-based inference: from local distortions to global model checksCode2
Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal PropertiesCode2
Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any GranularityCode2
Quantized symbolic time series approximationCode2
Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and BenchmarksCode2
LogLLM: Log-based Anomaly Detection Using Large Language ModelsCode2
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly DetectionCode2
ResAD: A Simple Framework for Class Generalizable Anomaly DetectionCode2
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial ScenariosCode2
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency PatchingCode2
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly DetectionCode2
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection FrameworkCode2
Self-supervised Anomaly Detection Pretraining Enhances Long-tail ECG DiagnosisCode2
CSAD: Unsupervised Component Segmentation for Logical Anomaly DetectionCode2
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series AnalysisCode2
GeneralAD: Anomaly Detection Across Domains by Attending to Distorted FeaturesCode2
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentationCode2
Follow the Rules: Reasoning for Video Anomaly Detection with Large Language ModelsCode2
Odd-One-Out: Anomaly Detection by Comparing with NeighborsCode2
European Space Agency Benchmark for Anomaly Detection in Satellite TelemetryCode2
Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A BenchmarkCode2
Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMCode2
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly DetectionCode2
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2Code2
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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