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 801–825 of 4856 papers

TitleStatusHype
CableInspect-AD: An Expert-Annotated Anomaly Detection DatasetCode1
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection AlgorithmsCode1
Peri-midFormer: Periodic Pyramid Transformer for Time Series AnalysisCode1
PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection DatasetCode1
RbA: Segmenting Unknown Regions Rejected by AllCode1
Pixel-wise Anomaly Detection in Complex Driving ScenesCode1
BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly DetectionCode1
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly DetectionCode1
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open ChallengesCode1
Positional Encoding in Transformer-Based Time Series Models: A SurveyCode1
An Incremental Unified Framework for Small Defect InspectionCode1
Are we certain it's anomalous?Code1
Power System Anomaly Detection and Classification Utilizing WLS-EKF State Estimation and Machine LearningCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor DetectionCode1
Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much BetterCode1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality DetectionCode1
PUAD: Frustratingly Simple Method for Robust Anomaly DetectionCode1
PyOD: A Python Toolbox for Scalable Outlier DetectionCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
BMAD: Benchmarks for Medical Anomaly DetectionCode1
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial LearningCode1
AnoDFDNet: A Deep Feature Difference Network for Anomaly DetectionCode1
Quo Vadis, Anomaly Detection? LLMs and VLMs in the SpotlightCode1
Broiler-Net: A Deep Convolutional Framework for Broiler Behavior Analysis in Poultry HousesCode1
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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