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 151–175 of 703 papers

TitleStatusHype
Quantile-based Maximum Likelihood Training for Outlier DetectionCode0
Quantifying Outlierness of Funds from their Categories using Supervised Similarity—0
Learning on Graphs with Out-of-Distribution NodesCode1
Uncertainty Quantification for Image-based Traffic Prediction across CitiesCode1
A Review of Change of Variable Formulas for Generative Modeling—0
Synthetic outlier generation for anomaly detection in autonomous driving—0
Image Outlier Detection Without Training using RANSACCode0
Edgewise outliers of network indexed signalsCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Robust Data Clustering with Outliers via Transformed Tensor Low-Rank RepresentationCode0
Anomaly Detection with Selective Dictionary LearningCode0
Outlier detection in regression: conic quadratic formulations—0
Training Ensembles with Inliers and Outliers for Semi-supervised Active LearningCode0
That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering—0
Robust Uncertainty Estimation for Classification of Maritime Objects—0
Computationally Assisted Quality Control for Public Health Data StreamsCode1
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structuresCode1
Anomaly Detection in Networks via Score-Based Generative ModelsCode0
Cascade Subspace Clustering for Outlier Detection—0
Female mosquito detection by means of AI techniques inside release containers in the context of a Sterile Insect Technique program—0
Kernel Random Projection Depth for Outlier Detection—0
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator—0
WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining—0
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction—0
Hierarchical Multiresolution Feature- and Prior-based Graphs for Classification—0
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All datasetsECG5000Balance scale_class 1Breast cancer Wisconsin_class 2Breast cancer Wisconsin_class 4Fashion-MNISTGlass identificationHeart-CHepatitisInternet AdIonosphere_class bSKAB

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