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 201–225 of 703 papers

TitleStatusHype
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots—0
Outlier Detection Using Generative Models with Theoretical Performance Guarantees—0
Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN—0
Tight Rates in Supervised Outlier Transfer Learning—0
Data Cleaning and Machine Learning: A Systematic Literature ReviewCode0
LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis—0
Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning—0
Distribution and volume based scoring for Isolation ForestsCode0
Outlier-Insensitive Kalman Filtering: Theory and ApplicationsCode0
Boundary Peeling: Outlier Detection Method Using One-Class Peeling—0
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel GraphsCode0
Large-scale gradient-based training of Mixtures of Factor Analyzers—0
Quantile-based Maximum Likelihood Training for Outlier DetectionCode0
Quantifying Outlierness of Funds from their Categories using Supervised Similarity—0
Synthetic outlier generation for anomaly detection in autonomous driving—0
A Review of Change of Variable Formulas for Generative Modeling—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
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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