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 351–375 of 703 papers

TitleStatusHype
Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN—0
Practical applications of metric space magnitude and weighting vectors—0
Prediction and outlier detection in classification problems—0
Probabilistic Outlier Detection and Generation—0
Probabilistic Robust Autoencoders for Outlier Detection—0
Prompt-driven efficient Open-set Semi-supervised Learning—0
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain—0
Provable Self-Representation Based Outlier Detection in a Union of Subspaces—0
PS-EIP: Robust Photometric Stereo Based on Event Interval Profile—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning—0
Quantifying Outlierness of Funds from their Categories using Supervised Similarity—0
Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images—0
Random Subspace Learning Approach to High-Dimensional Outliers Detection—0
RANRAC: Robust Neural Scene Representations via Random Ray Consensus—0
Rapid Distance-Based Outlier Detection via Sampling—0
RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies—0
Real-Time Outlier Detection with Dynamic Process Limits—0
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval—0
RECol: Reconstruction Error Columns for Outlier Detection—0
Rectifying Self Organizing Maps for Automatic Concept Learning from Web Images—0
Re-experiment Smart: a Novel Method to Enhance Data-driven Prediction of Mechanical Properties of Epoxy Polymers—0
Regularized Contrastive Partial Multi-view Outlier Detection—0
Relative Density-Ratio Estimation for Robust Distribution Comparison—0
ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals—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