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 2201–2250 of 4856 papers

TitleStatusHype
Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection—0
ASTD Patterns for Integrated Continuous Anomaly Detection In Data Logs—0
A Stacked Autoencoder Neural Network based Automated Feature Extraction Method for Anomaly detection in On-line Condition Monitoring—0
Anomalous Sound Detection Based on Machine Activity Detection—0
Distributed optimization in wireless sensor networks: an island-model framework—0
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control—0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
Distributed Deep Learning for Persistent Monitoring of agricultural Fields—0
AssistPDA: An Online Video Surveillance Assistant for Video Anomaly Prediction, Detection, and Analysis—0
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT—0
Distributed Anomaly Detection in Modern Power Systems: A Penalty-based Mitigation Approach—0
Distributed Anomaly Detection and Estimation over Sensor Networks: Observational-Equivalence and Q-Redundant Observer Design—0
Distilling the Posterior in Bayesian Neural Networks—0
Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study—0
AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection—0
Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection—0
A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset—0
Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks—0
A spectral-spatial fusion anomaly detection method for hyperspectral imagery—0
Anomalous Example Detection in Deep Learning: A Survey—0
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection—0
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction—0
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts—0
Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motors—0
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification—0
Anomalous Client Detection in Federated Learning—0
Discriminative-Generative Representation Learning for One-Class Anomaly Detection—0
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning—0
Discriminative-Generative Dual Memory Video Anomaly Detection—0
Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection—0
Anomalous Change Point Detection Using Probabilistic Predictive Coding—0
Discriminative Deep Random Walk for Network Classification—0
Discrete neural representations for explainable anomaly detection—0
A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys—0
Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI—0
Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection—0
A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data—0
Anomalies, Representations, and Self-Supervision—0
Sequential Adversarial Anomaly Detection for One-Class Event Data—0
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses—0
A self-supervised text-vision framework for automated brain abnormality detection—0
Directional anomaly detection—0
A Self-Supervised Framework for Space Object Behaviour Characterisation—0
Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation—0
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection—0
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels—0
Dimensionality reduction techniques to support insider trading detection—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—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