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 1401–1425 of 4856 papers

TitleStatusHype
Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization—0
CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks—0
Making Anomalies More Anomalous: Video Anomaly Detection Using a Novel Generator and DestroyerCode0
Uncertainty Quantification in Anomaly Detection with Cross-Conformal p-ValuesCode0
TOTEM: TOkenized Time Series EMbeddings for General Time Series AnalysisCode3
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management—0
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and EvaluationCode1
PatchFlow: Leveraging a Flow-Based Model with Patch Features—0
Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection—0
PUAD: Frustratingly Simple Method for Robust Anomaly DetectionCode1
Reimagining Anomalies: What If Anomalies Were Normal?—0
Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly DetectionCode1
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework—0
Statistical Test on Diffusion Model-based Anomaly Detection by Selective Inference—0
Generative Semi-supervised Graph Anomaly DetectionCode2
SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised LearningCode1
Empirical Density Estimation based on Spline Quasi-Interpolation with applications to Copulas clustering modeling—0
Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUICode0
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection ModelsCode2
LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs—0
Backdoor Attack against One-Class Sequential Anomaly Detection ModelsCode0
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review—0
Trustworthy UAV Cooperative Localization: Information Analysis of Performance and Security—0
Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data—0
Detecting Anomalous Events in Object-centric Business Processes via Graph Neural NetworksCode0
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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