SOTAVerified

Outlier Detection

Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: Coverage-based Outlier Explanation

Papers

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Showing 601–625 of 703 papers

TitleStatusHype
Contextual Outlier Interpretation—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
Contextual Unsupervised Outlier Detection in Sequences—0
Continual Learning with Fully Probabilistic Models—0
Coverage-based Outlier Explanation—0
Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach—0
Cross Domain Image Matching in Presence of Outliers—0
Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
Data Stream Clustering: A Review—0
Dealing with Class Imbalance using Thresholding—0
Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control—0
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes—0
Deep Learning for Anomaly Detection: A Review—0
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments—0
Deep Learning with Sets and Point Clouds—0
Deep Sequence Modeling for Anomalous ISP Traffic Prediction—0
Deep Variational Semi-Supervised Novelty Detection—0
Toward Scalable and Unified Example-based Explanation and Outlier Detection—0
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction—0
Dense outlier detection and open-set recognition based on training with noisy negative images—0
Detecting abnormal events in video using Narrowed Normality Clusters—0
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation—0
Detecting outliers by clustering algorithms—0
Detecting Outliers in Data with Correlated Measures—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98—Unverified
2F-t ALSTM-FCNAccuracy0.95—Unverified
3GENDISAccuracy0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6—Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74—Unverified