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 1676–1700 of 4856 papers

TitleStatusHype
Directional anomaly detection—0
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses—0
A self-supervised text-vision framework for automated brain abnormality detection—0
Entropic one-class classifiers—0
Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI—0
Discrete neural representations for explainable anomaly detection—0
Discriminative Deep Random Walk for Network Classification—0
Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection—0
Discriminative-Generative Dual Memory Video Anomaly Detection—0
Discriminative-Generative Representation Learning for One-Class Anomaly Detection—0
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning—0
Data-Efficient and Interpretable Tabular Anomaly Detection—0
Data-Driven Thermal Modelling for Anomaly Detection in Electric Vehicle Charging Stations—0
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction—0
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection—0
Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks—0
Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection—0
Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering—0
Distributed Anomaly Detection and Estimation over Sensor Networks: Observational-Equivalence and Q-Redundant Observer Design—0
Distributed Anomaly Detection in Modern Power Systems: A Penalty-based Mitigation Approach—0
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT—0
Distributed Deep Learning for Persistent Monitoring of agricultural Fields—0
Data-Driven Semi-Supervised Machine Learning with Safety Indicators for Abnormal Driving Behavior 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