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 3851–3875 of 4856 papers

TitleStatusHype
Network Traffic Decomposition for Anomaly Detection—0
Network Volume Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking—0
Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection—0
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks—0
Neural Embedding: Learning the Embedding of the Manifold of Physics Data—0
neuralGAM: An R Package for Fitting Generalized Additive Neural Networks—0
Neural Memory Plasticity for Anomaly Detection—0
Neural Network-based Quantization for Network Automation—0
Neural Networks, Artificial Intelligence and the Computational Brain—0
NEURO HAND: A weakly supervised Hierarchical Attention Network for interpretable neuroimaging abnormality Detection—0
Neuromorphic Architecture for the Hierarchical Temporal Memory—0
Neuromorphic implementation of ECG anomaly detection using delay chains—0
Neuromorphic IoT Architecture for Efficient Water Management: A Smart Village Case Study—0
Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions—0
Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems—0
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0—0
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics—0
NLP Based Anomaly Detection for Categorical Time Series—0
No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection—0
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection—0
Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands—0
Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs—0
Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments—0
Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection—0
Noise-Resistant Video Anomaly Detection via RGB Error-Guided Multiscale Predictive Coding and Dynamic Memory—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