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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 1876–1900 of 4856 papers

TitleStatusHype
Sparse Binary Transformers for Multivariate Time Series Modeling—0
Multi-Class Deep SVDD: Anomaly Detection Approach in Astronomy with Distinct Inlier CategoriesCode1
Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection—0
Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection—0
Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization—0
A Deep-Learning Method Using Auto-encoder and Generative Adversarial Network for Anomaly Detection on Ancient Stone Stele Surfaces—0
Implementing Immune Repertoire Models Using Weighted Finite State Machines—0
Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques—0
Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly DetectionCode1
Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA GraphCode0
Anomaly Detection in Global Financial Markets with Graph Neural Networks and Nonextensive Entropy—0
Synthetic outlier generation for anomaly detection in autonomous driving—0
Discriminative Graph-level Anomaly Detection via Dual-students-teacher ModelCode0
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain ImagesCode1
UGainS: Uncertainty Guided Anomaly Instance SegmentationCode1
Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly DetectionCode1
Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomaliesCode0
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization ApproachCode1
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach—0
Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel—0
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain—0
Patch-wise Auto-Encoder for Visual Anomaly Detection—0
PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps—0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model—0
General Anomaly Detection of Underwater Gliders Validated by Large-scale Deployment Datasets—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