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

Showing 13511375 of 4856 papers

TitleStatusHype
Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite RankingCode0
An Adaptive Anaphylaxis Detection and Emergency Response SystemCode0
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly DetectionCode0
GANomaly: Semi-Supervised Anomaly Detection via Adversarial TrainingCode0
Graph Laplacian for Image Anomaly DetectionCode0
Concentration bounds for the empirical angular measure with statistical learning applicationsCode0
Anomaly Detection with Robust Deep AutoencodersCode0
Anomaly Detection through Unsupervised Federated LearningCode0
From Zero to Hero: Cold-Start Anomaly DetectionCode0
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based AlgorithmsCode0
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection MethodCode0
fSEAD: a Composable FPGA-based Streaming Ensemble Anomaly Detection LibraryCode0
FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event DetectionCode0
Computer Vision and Normalizing Flow-Based Defect DetectionCode0
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillationCode0
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical ObservationsCode0
Is AUC the best measure for practical comparison of anomaly detectors?Code0
Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite ImagesCode0
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methodsCode0
Composite Convolution: a Flexible Operator for Deep Learning on 3D Point CloudsCode0
Finite sample guarantees for quantile estimation: An application to detector threshold tuningCode0
CKNN: Cleansed k-Nearest Neighbor for Unsupervised Video Anomaly DetectionCode0
Foundation Models for Structural Health MonitoringCode0
Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path SignatureCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified