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
A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows—0
A Systematic Mapping Study in AIOps—0
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions—0
ATAC-Net: Zoomed view works better for Anomaly Detection—0
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference—0
A task of anomaly detection for a smart satellite Internet of things system—0
A Taxonomy of Anomalies in Log Data—0
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels—0
A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments—0
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure—0
A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph—0
Atom dimension adaptation for infinite set dictionary learning—0
A Transfer Learning Framework for Anomaly Detection Using Model of Normality—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
Attack-Agnostic Adversarial Detection—0
Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches—0
Attacking Face Recognition with T-shirts: Database, Vulnerability Assessment and Detection—0
AttackLLM: LLM-based Attack Pattern Generation for an Industrial Control System—0
Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning—0
Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection—0
Attention-Based Self-Supervised Feature Learning for Security Data—0
Attention Boosted Autoencoder for Building Energy Anomaly Detection—0
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos—0
Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detection—0
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management—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