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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1276–1300 of 4856 papers

TitleStatusHype
TSLANet: Rethinking Transformers for Time Series Representation LearningCode3
HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies—0
Context-aware Video Anomaly Detection in Long-Term Datasets—0
Language Models Meet Anomaly Detection for Better Interpretability and GeneralizabilityCode0
Anomaly Detection in Power Grids via Context-Agnostic Learning—0
3D-CSAD: Untrained 3D Anomaly Detection for Complex Manufacturing Surfaces—0
SplatPose & Detect: Pose-Agnostic 3D Anomaly DetectionCode1
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly DetectionCode3
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability—0
EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection—0
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly DetectionCode3
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey—0
TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis—0
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning—0
Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly DetectionCode1
Time topological analysis of EEG using signature theory—0
A Deep Look Into -- Automated Lung X-Ray Abnormality Detection System—0
MedIAnomaly: A comparative study of anomaly detection in medical imagesCode2
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold—0
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly DetectionCode0
Test Time Training for Industrial Anomaly Segmentation—0
Learning with errors based dynamic encryption that discloses residue signal for anomaly detection—0
Foundation Models for Structural Health MonitoringCode0
Transfer learning applications for anomaly detection in wind turbines—0
TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly DetectionCode0
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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