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 4676–4700 of 4856 papers

TitleStatusHype
Exact Optimization of Conformal Predictors via Incremental and Decremental LearningCode0
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on GraphsCode0
Anomaly detection in radio galaxy data with trainable COSFIRE filtersCode0
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System TelemetryCode0
A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless FunctionsCode0
Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal FeaturesCode0
Performance Metric for Multiple Anomaly Score Distributions with Discrete Severity LevelsCode0
Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security ApplicationsCode0
Exact Matrix Seriation through Mathematical Optimization: Stress and Effectiveness-Based ModelsCode0
TopoAct: Visually Exploring the Shape of Activations in Deep LearningCode0
SIFT and SURF based feature extraction for the anomaly detectionCode0
Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasetsCode0
Anomaly Detection in Networks via Score-Based Generative ModelsCode0
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS DiagnosisCode0
Signal Novelty Detection as an Intrinsic Reward for RoboticsCode0
A Meta-Analysis of the Anomaly Detection ProblemCode0
Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural NetworksCode0
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETCode0
Topological Pooling on GraphsCode0
A Uniform Framework for Anomaly Detection in Deep Neural NetworksCode0
Audio-Visual Dataset and Method for Anomaly Detection in Traffic VideosCode0
Video Anomaly Detection with Structured KeywordsCode0
Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly DetectionCode0
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly DetectionCode0
PICASO: Permutation-Invariant Cascaded Attentional Set OperatorCode0
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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