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 3676–3700 of 4856 papers

TitleStatusHype
A Physics-Based Context-Aware Approach for Anomaly Detection in Teleoperated Driving Operations Under False Data Injection Attacks—0
A Poisson Process AutoDecoder for X-ray Sources—0
Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection—0
Application Of ADNN For Background Subtraction In Smart Surveillance System—0
Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography—0
Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning—0
Application of MUSIC-type imaging for anomaly detection without background information—0
Application of Unsupervised Domain Adaptation for Structural MRI Analysis—0
Applications of Artificial Intelligence, Machine Learning and related techniques for Computer Networking Systems—0
Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review—0
Applications of Machine Learning to the Identification of Anomalous ER Claims—0
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection—0
Applications of Signature Methods to Market Anomaly Detection—0
Applied Bayesian Structural Health Monitoring: inclinometer data anomaly detection and forecasting—0
Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes—0
Intelligent Approaches to Predictive Analytics in Occupational Health and Safety in India—0
Applying Quantum Autoencoders for Time Series Anomaly Detection—0
Approaching adverse event detection utilizing transformers on clinical time-series—0
Approximate Maximum Halfspace Discrepancy—0
Approximating DTW with a convolutional neural network on EEG data—0
Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection—0
A principled distributional approach to trajectory similarity measurement—0
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy—0
A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks—0
A Random Matrix Theoretical Approach to Early Event Detection in Smart Grid—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