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 101125 of 4856 papers

TitleStatusHype
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2Code2
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly DetectionCode2
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial ScenariosCode2
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly DetectionCode2
Anomaly Transformer: Time Series Anomaly Detection with Association DiscrepancyCode2
AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLMCode2
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational AutoencoderCode2
One-for-More: Continual Diffusion Model for Anomaly DetectionCode2
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly DetectionCode2
Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly DetectionCode2
Quantized symbolic time series approximationCode2
Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language ModelsCode2
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly DetectionCode2
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic ThresholdingCode2
DiAD: A Diffusion-based Framework for Multi-class Anomaly DetectionCode2
A Survey on Diffusion Models for Anomaly DetectionCode2
Rethinking Graph Neural Networks for Anomaly DetectionCode2
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesCode2
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly DetectionCode2
Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting MaskCode2
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly DetectionCode2
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost FilteringCode2
A Unified Model for Multi-class Anomaly DetectionCode2
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
7DDADDetection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9PBASDetection AUROC99.8Unverified
10HETMMDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4DDADDetection AUROC98.9Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
6INP-Former ViT-B (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