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 3201–3225 of 4856 papers

TitleStatusHype
Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review—0
Machine Learning based Anomaly Detection for Smart Shirt: A Systematic Review—0
Object-centric and memory-guided normality reconstruction for video anomaly detection—0
Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis—0
Coresets for Data Discretization and Sine Wave Fitting—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods—0
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—0
Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health MonitoringCode0
Data-Efficient and Interpretable Tabular Anomaly Detection—0
Anomaly Detection in Big Data—0
Unsupervised Anomaly Detection from Time-of-Flight Depth Images—0
Efficient Dynamic Clustering: Capturing Patterns from Historical Cluster Evolution—0
Addressing Gap between Training Data and Deployed Environment by On-Device LearningCode0
Anomaly Detection in File Fragment Classification of Image File Formats—0
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
Self-Supervised and Interpretable Anomaly Detection using Network Transformers—0
Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection—0
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection—0
Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection—0
Do autoencoders need a bottleneck for anomaly detection?—0
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations—0
A spectral-spatial fusion anomaly detection method for hyperspectral imagery—0
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors—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