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 4326–4350 of 4856 papers

TitleStatusHype
DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning—0
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities—0
Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems—0
Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data—0
Driving Anomaly Detection Using Conditional Generative Adversarial Network—0
DROCC: Deep Robust One-Class Classification—0
Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security—0
Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data—0
Dual-encoder Bidirectional Generative Adversarial Networks for Anomaly Detection—0
Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection—0
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation—0
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection—0
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage—0
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection—0
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network—0
Dynamic Bayesian Approach for decision-making in Ego-Things—0
Dynamic Graph Embedding via LSTM History Tracking—0
Dynamic Interactional And Cooperative Network For Shield Machine—0
DynamoPMU: A Physics Informed Anomaly Detection and Prediction Methodology using non-linear dynamics from μPMU Measurement Data—0
Dysarthric speech evaluation: automatic and perceptual approaches—0
eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems—0
EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection—0
EAPCR: A Universal Feature Extractor for Scientific Data without Explicit Feature Relation Patterns—0
Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms—0
Early Anomaly Detection in Power Systems Based on Random Matrix Theory—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