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 1351–1400 of 4856 papers

TitleStatusHype
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
Detecting Anomalous Invoice Line Items in the Legal Case Lifecycle—0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks—0
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)—0
DFM: Interpolant-free Dual Flow Matching—0
Applications of Signature Methods to Market Anomaly Detection—0
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection—0
A Neural Network Anomaly Detector Using the Random Cluster Model—0
Applications of Machine Learning to the Identification of Anomalous ER Claims—0
Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review—0
An Ensemble Approach toward Automated Variable Selection for Network Anomaly Detection—0
ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency—0
Applications of Artificial Intelligence, Machine Learning and related techniques for Computer Networking Systems—0
Application of Unsupervised Domain Adaptation for Structural MRI Analysis—0
An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks—0
Application of MUSIC-type imaging for anomaly detection without background information—0
Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning—0
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning—0
AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework—0
Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography—0
Application Of ADNN For Background Subtraction In Smart Surveillance System—0
Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection—0
A Poisson Process AutoDecoder for X-ray Sources—0
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing—0
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection—0
A Physics-Based Context-Aware Approach for Anomaly Detection in Teleoperated Driving Operations Under False Data Injection Attacks—0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection—0
A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection—0
APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks—0
An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs—0
A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems—0
A Bayesian Ensemble for Unsupervised Anomaly Detection—0
Detecting Anomalies Through Contrast in Heterogeneous Data—0
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection—0
ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels—0
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions—0
Anytime Hierarchical Clustering—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