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 3376–3400 of 4856 papers

TitleStatusHype
Analysis of Vision-based Abnormal Red Blood Cell Classification—0
Analytical Discovery of Manifold with Machine Learning—0
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks—0
Analytical Probability Distributions and EM-Learning for Deep Generative Networks—0
Analytics and Machine Learning Powered Wireless Network Optimization and Planning—0
Analyzing Business Process Anomalies Using Autoencoders—0
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications—0
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction—0
An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series—0
An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems—0
An Attention Free Conditional Autoencoder For Anomaly Detection in Cryptocurrencies—0
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance—0
An Automated Analysis Framework for Trajectory Datasets—0
An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data—0
An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction—0
An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone—0
An Autonomous Drone Swarm for Detecting and Tracking Anomalies among Dense Vegetation—0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
An Efficient Approach for Anomaly Detection in Traffic Videos—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
An Efficient One-Class SVM for Anomaly Detection in the Internet of Things—0
An Efficient Outlier Detection Algorithm for Data Streaming—0
An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs—0
Show:102550
← PrevPage 136 of 195Next →
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