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 3326–3350 of 4856 papers

TitleStatusHype
AI-Powered Anomaly Detection with Blockchain for Real-Time Security and Reliability in Autonomous Vehicles—0
A Kernel-Based Nonparametric Test for Anomaly Detection over Line Networks—0
A Kernel Two-sample Test for Dynamical Systems—0
AKM^2D : An Adaptive Framework for Online Sensing and Anomaly Quantification—0
A landmark-based algorithm for automatic pattern recognition and abnormality detection—0
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables—0
ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN—0
Algorithmic Recourse for Anomaly Detection in Multivariate Time Series—0
A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement—0
A Lightweight Video Anomaly Detection Model with Weak Supervision and Adaptive Instance Selection—0
A Lightweight Yet Robust Approach to Textual Anomaly Detection—0
Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators—0
ALOJA: A Framework for Benchmarking and Predictive Analytics in Big Data Deployments—0
ALOJA-ML: A Framework for Automating Characterization and Knowledge Discovery in Hadoop Deployments—0
A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks—0
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection—0
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts—0
A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
A Machine-Synesthetic Approach To DDoS Network Attack Detection—0
AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data—0
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection—0
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays—0
A Mallows-like Criterion for Anomaly Detection with Random Forest Implementation—0
A Marketplace Price Anomaly Detection System at Scale—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