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 3201–3225 of 4856 papers

TitleStatusHype
A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods—0
Activation Learning by Local Competitions—0
Active anomaly detection based on deep one-class classification—0
Active Anomaly Detection for time-domain discoveries—0
Active Anomaly Detection with Switching Cost—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
Active Learning for Network Intrusion Detection—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks—0
Activity-Guided Industrial Anomalous Sound Detection against Interferences—0
Activity report analysis with automatic single or multispan answer extraction—0
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation—0
ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model—0
Adaptable and Interpretable Framework for Novelty Detection in Real-Time IoT Systems—0
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection—0
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing—0
Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case—0
Adaptive Cost-sensitive Online Classification—0
Adaptive Fault Tolerance Mechanisms of Large Language Models in Cloud Computing Environments—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
Adaptive Immunity for Software: Towards Autonomous Self-healing Systems—0
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
Adaptive Modeling of Satellite-Derived Nighttime Lights Time-Series for Tracking Urban Change Processes Using Machine Learning—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