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 1276–1300 of 4856 papers

TitleStatusHype
Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection—0
Enhancing anomaly detection with topology-aware autoencoders—0
Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities—0
Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection—0
Privacy-Preserving Hybrid Ensemble Model for Network Anomaly Detection: Balancing Security and Data Protection—0
GenIAS: Generator for Instantiating Anomalies in time Series—0
CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection—0
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data—0
Advancing climate model interpretability: Feature attribution for Arctic melt anomalies—0
FADE: Forecasting for Anomaly Detection on ECGCode0
Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models—0
Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning—0
Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection—0
Foundation Models for Anomaly Detection: Vision and Challenges—0
Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection—0
Aero-engines Anomaly Detection using an Unsupervised Fisher Autoencoder—0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated LearningCode0
DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions—0
Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks—0
NLP-Based .NET CLR Event Logs AnalyzerCode0
From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping—0
Position: Untrained Machine Learning for 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
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