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 2651–2675 of 4856 papers

TitleStatusHype
Spatially-weighted Anomaly Detection—0
Spatially-weighted Anomaly Detection with Regression Model—0
Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles—0
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities—0
Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality—0
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter—0
Spatio-Temporal-based Context Fusion for Video Anomaly Detection—0
Spatio-Temporal Correlation Analysis of Online Monitoring Data for Anomaly Detection and Location in Distribution Networks—0
Spatio-temporal predictive tasks for abnormal event detection in videos—0
Spatio-Temporal Relation Learning for Video Anomaly Detection—0
Spatio-temporal Video Parsing for Abnormality Detection—0
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks—0
Spectral embedding of weighted graphs—0
Spectral-Spatial Extraction through Layered Tensor Decomposition for Hyperspectral Anomaly Detection—0
Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI—0
SPICED: Syntactical Bug and Trojan Pattern Identification in A/MS Circuits using LLM-Enhanced Detection—0
SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection—0
Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving—0
Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things—0
SIAD: Self-supervised Image Anomaly Detection System—0
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection—0
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies—0
Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection—0
STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection—0
State Frequency Estimation for Anomaly Detection—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