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 3901–3925 of 4856 papers

TitleStatusHype
Omni-Dimensional Frequency Learner for General Time Series Analysis—0
On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach—0
On Anomaly Ranking and Excess-Mass Curves—0
On-device Anomaly Detection in Conveyor Belt Operations—0
One-class anomaly detection through color-to-thermal AI for building envelope inspection—0
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks—0
One-class Steel Detector Using Patch GAN Discriminator for Visualising Anomalous Feature Map—0
One-Class Support Measure Machines for Group Anomaly Detection—0
One-Class Support Measure Machines for Group Anomaly Detection—0
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities—0
One-Class SVM with Privileged Information and its Application to Malware Detection—0
OneLog: Towards End-to-End Training in Software Log Anomaly Detection—0
One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments—0
ONER: Online Experience Replay for Incremental Anomaly Detection—0
One-Shot GAN Generated Fake Face Detection—0
On Identifying Anomalies in Tor Usage with Applications in Detecting Internet Censorship—0
Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly—0
Online Anomaly Detection over Live Social Video Streaming—0
Online Anomaly Detection via Class-Imbalance Learning—0
Online Anomaly Detection with Sparse Gaussian Processes—0
Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems—0
Online Data Thinning via Multi-Subspace Tracking—0
Online Electric Vehicle Charging Detection Based on Memory-based Transformer using Smart Meter Data—0
Online false discovery rate control for anomaly detection in time series—0
Online learnability of Statistical Relational Learning in 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