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

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Showing 2276–2300 of 4856 papers

TitleStatusHype
Sketching Multidimensional Time Series for Fast Discord Mining—0
AIOps-Driven Enhancement of Log Anomaly Detection in Unsupervised Scenarios—0
MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network—0
Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals—0
Model-driven Engineering for Machine Learning Components: A Systematic Literature Review—0
Architecture of Data Anomaly Detection-Enhanced Decentralized Expert System for Early-Stage Alzheimer's Disease Prediction—0
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection—0
Few-shot time-series anomaly detection with unsupervised domain adaptation—0
Log-based Anomaly Detection of Enterprise Software: An Empirical Study—0
A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks—0
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection—0
Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental LearningCode0
MENTOR: Human Perception-Guided Pretraining for Increased Generalization—0
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach—0
MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic—0
Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling—0
Understanding Parameter Saliency via Extreme Value Theory—0
Detecting subtle cyberattacks on adaptive cruise control vehicles: A machine learning approach—0
OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User—0
MIM-GAN-based Anomaly Detection for Multivariate Time Series DataCode0
GADY: Unsupervised Anomaly Detection on Dynamic Graphs—0
On Pixel-level Performance Assessment in Anomaly Detection—0
One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments—0
Localizing Anomalies in Critical Infrastructure using Model-Based Drift ExplanationsCode0
ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency—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