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 1601–1625 of 4856 papers

TitleStatusHype
Diagnosis driven Anomaly Detection for CPS—0
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection—0
BatchNorm-based Weakly Supervised Video Anomaly DetectionCode1
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly DetectionCode0
Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning NetworkCode1
Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter ViewCode1
Anomaly detection in cross-country money transfer temporal networks—0
Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks—0
Set Features for Anomaly DetectionCode1
Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation—0
Video Anomaly Detection using GAN—0
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators—0
Explainable Time Series Anomaly Detection using Masked Latent Generative ModelingCode1
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET—0
LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly DetectorCode1
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly GenerationCode1
Correlated Attention in Transformers for Multivariate Time Series—0
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance InspectionCode0
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI—0
Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain SolutionCode1
SORTAD: Self-Supervised Optimized Random Transformations for Anomaly Detection in Tabular Data—0
Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real WorldCode0
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection—0
Surprisal Driven k-NN for Robust and Interpretable Nonparametric Learning—0
TransFusion -- A Transparency-Based Diffusion Model for Anomaly DetectionCode1
Show:102550
← PrevPage 65 of 195Next →
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