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 3776–3800 of 4856 papers

TitleStatusHype
MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detection—0
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark—0
MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network—0
MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning—0
Multi-branch Neural Networks for Video Anomaly Detection in Adverse Lighting and Weather Conditions—0
Multicalibrated Partitions for Importance Weights—0
Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation—0
Stack of discriminative autoencoders for multiclass anomaly detection in endoscopy images—0
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data—0
Multi-Class Multiple Instance Learning for Predicting Precursors to Aviation Safety Events—0
Multi-Contextual Predictions with Vision Transformer for Video Anomaly Detection—0
Multi-criteria Anomaly Detection using Pareto Depth Analysis—0
Multi-criteria Similarity-based Anomaly Detection using Pareto Depth Analysis—0
Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Industrial Anomaly Detection—0
Multilevel Anomaly Detection for Mixed Data—0
Multi-Level Anomaly Detection on Time-Varying Graph Data—0
Multi-level conformal clustering: A distribution-free technique for clustering and anomaly detection—0
Multi-level hypothesis testing for populations of heterogeneous networks—0
Multi-level Memory-augmented Appearance-Motion Correspondence Framework for Video Anomaly Detection—0
Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection—0
Multimedia Datasets for Anomaly Detection: A Review—0
Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots—0
Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised Anomaly Violence Detection—0
Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models—0
Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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