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 701–725 of 4856 papers

TitleStatusHype
ASTD Patterns for Integrated Continuous Anomaly Detection In Data Logs—0
Early Prediction of Natural Gas Pipeline Leaks Using the MKTCN Model—0
GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly DetectionCode1
Predictive Digital Twin for Condition Monitoring Using Thermal Imaging—0
Machine learning-driven Anomaly Detection and Forecasting for Euclid Space Telescope Operations—0
Interpretable Measurement of CNN Deep Feature Density using Copula and the Generalized Characteristic Function—0
Peri-midFormer: Periodic Pyramid Transformer for Time Series AnalysisCode1
From CNN to CNN + RNN: Adapting Visualization Techniques for Time-Series Anomaly Detection—0
Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound ImagingCode0
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis—0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches—0
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers—0
High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel ApproachCode0
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community DetectionCode0
Anomalous Client Detection in Federated Learning—0
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold NetworksCode1
Identify Backdoored Model in Federated Learning via Individual UnlearningCode1
AAD-LLM: Adaptive Anomaly Detection Using Large Language Models—0
AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal PropertiesCode0
Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions—0
Directional anomaly detection—0
PV-VTT: A Privacy-Centric Dataset for Mission-Specific Anomaly Detection and Natural Language Interpretation—0
MIXAD: Memory-Induced Explainable Time Series Anomaly DetectionCode0
Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly DetectorCode0
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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