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

Showing 24262450 of 4856 papers

TitleStatusHype
A Novel Data Pre-processing Technique: Making Data Mining Robust to Different Units and Scales of Measurement0
A Novel Representation of Periodic Pattern and Its Application to Untrained Anomaly Detection0
Anticipated Network Surveillance -- An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework0
An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything0
An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS0
ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data0
AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings0
Any-Shot Sequential Anomaly Detection in Surveillance Videos0
Anytime Hierarchical Clustering0
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection0
APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection0
A Physics-Based Context-Aware Approach for Anomaly Detection in Teleoperated Driving Operations Under False Data Injection Attacks0
A Poisson Process AutoDecoder for X-ray Sources0
Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection0
Application Of ADNN For Background Subtraction In Smart Surveillance System0
Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography0
Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning0
Application of MUSIC-type imaging for anomaly detection without background information0
Application of Unsupervised Domain Adaptation for Structural MRI Analysis0
Applications of Artificial Intelligence, Machine Learning and related techniques for Computer Networking Systems0
Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review0
Applications of Machine Learning to the Identification of Anomalous ER Claims0
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified