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 4076–4100 of 4856 papers

TitleStatusHype
Cross-Domain Learning for Video Anomaly Detection with Limited Supervision—0
Cross-Domain Video Anomaly Detection without Target Domain Adaptation—0
Cross-Entropy Games for Language Models: From Implicit Knowledge to General Capability Measures—0
Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security—0
Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection—0
Crowded Scene Analysis: A Survey—0
Crowd-level Abnormal Behavior Detection via Multi-scale Motion Consistency Learning—0
Crowd Scene Analysis using Deep Learning Techniques—0
CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System—0
CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation—0
Curved Geometric Networks for Visual Anomaly Recognition—0
CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection—0
Custom DNN using Reward Modulated Inverted STDP Learning for Temporal Pattern Recognition—0
Anomaly Anything: Promptable Unseen Visual Anomaly Generation—0
CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection—0
Cyber Anomaly Detection Using Graph-node Role-dynamics—0
Cyber-Attack Event Analysis for EV Charging Stations—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Cybersecurity threat detection based on a UEBA framework using Deep Autoencoders—0
CyberSentinel: An Emergent Threat Detection System for AI Security—0
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection—0
DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation—0
DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection—0
Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance—0
Dance With Self-Attention: A New Look of Conditional Random Fields on Anomaly Detection in Videos—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