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 1326–1350 of 4856 papers

TitleStatusHype
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy—0
A principled distributional approach to trajectory similarity measurement—0
An Event based Prediction Suffix Tree—0
1D Convolutional Neural Networks and Applications: A Survey—0
Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection—0
Approximating DTW with a convolutional neural network on EEG data—0
ADSaS: Comprehensive Real-time Anomaly Detection System—0
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems—0
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection—0
Approximate Maximum Halfspace Discrepancy—0
Approaching adverse event detection utilizing transformers on clinical time-series—0
A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance—0
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization—0
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level—0
A Comprehensive Survey of Transformers for Computer Vision—0
Applying Quantum Autoencoders for Time Series Anomaly Detection—0
Intelligent Approaches to Predictive Analytics in Occupational Health and Safety in India—0
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices—0
Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes—0
Applied Bayesian Structural Health Monitoring: inclinometer data anomaly detection and forecasting—0
A neural-network based anomaly detection system and a safety protocol to protect vehicular network—0
ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems—0
Synthetic Time Series for Anomaly Detection in Cloud Microservices—0
Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems—0
Deep Representation Learning for Social Network Analysis—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