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 2726–2750 of 4856 papers

TitleStatusHype
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection—0
CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection—0
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection—0
Clear Memory-Augmented Auto-Encoder for Surface Defect Detection—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
Client-Specific Anomaly Detection for Face Presentation Attack Detection—0
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation—0
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection—0
Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing—0
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms—0
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series—0
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations—0
Clustering-based Anomaly Detection for microservices—0
Clustering Driven Deep Autoencoder for Video Anomaly Detection—0
Clustering of Time Series Data with Prior Geographical Information—0
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection—0
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection—0
CoDetect: Financial Fraud Detection With Anomaly Feature Detection—0
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection—0
Coincident Learning for Unsupervised Anomaly Detection—0
Collaborative Anomaly Detection—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—0
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
Combining Switching Mechanism with Re-Initialization and Anomaly Detection for Resiliency of Cyber-Physical Systems—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