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 201–250 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
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
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
GBG++: A Fast and Stable Granular Ball Generation Method for Classification—0
Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training StoppingCode0
Centering the Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection—0
Technical outlier detection via convolutional variational autoencoder for the ADMANI breast mammogram dataset—0
Non-parametric cumulants approach for outlier detection of multivariate financial data—0
Incremental Outlier Detection Modelling Using Streaming Analytics in Finance & Health Care—0
Separability and Scatteredness (S&S) Ratio-Based Efficient SVM Regularization Parameter, Kernel, and Kernel Parameter Selection—0
A Probabilistic Transformation of Distance-Based OutliersCode0
Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing—0
Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learning—0
Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System—0
HPSCAN: Human Perception-Based Scattered Data ClusteringCode0
Analyzing categorical time series with the R package ctsfeatures—0
Ordinal time series analysis with the R package otsfeatures—0
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities—0
An Improved Heart Disease Prediction Using Stacked Ensemble Method—0
PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data—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