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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2751–2775 of 4856 papers

TitleStatusHype
Community-based anomaly detection using spectral graph filtering—0
Community-Level Anomaly Detection for Anti-Money Laundering—0
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments—0
Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System—0
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network—0
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection—0
Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers—0
Comparison of Optimizers for Fault Isolation and Diagnostics of Control Rod Drives—0
Comparison of RNN Encoder-Decoder Models for Anomaly Detection—0
Competing Topic Naming Conventions in Quora: Predicting Appropriate Topic Merges and Winning Topics from Millions of Topic Pairs—0
ComplexVAD: Detecting Interaction Anomalies in Video—0
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition—0
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection—0
Composite Kernel Local Angular Discriminant Analysis for Multi-Sensor Geospatial Image Analysis—0
Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset—0
Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test—0
Compressed Smooth Sparse Decomposition—0
Compressive Change Retrieval for Moving Object Detection—0
Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation—0
Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey—0
Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts—0
Computing Graph Descriptors on Edge Streams—0
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots—0
Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets—0
Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model—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