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 101–125 of 703 papers

TitleStatusHype
About Test-time training for outlier detection—0
Hyperbolic Metric Learning for Visual Outlier Detection—0
Automatic Outlier Rectification via Optimal Transport—0
Validating and Exploring Large Geographic Corpora—0
Interactive Continual Learning: Fast and Slow ThinkingCode2
Outlier-Detection for Reactive Machine Learned Potential Energy SurfacesCode0
Outlier detection by ensembling uncertainty with negative objectnessCode1
A Comprehensive System for Secondary Structure Analysis of Protein Models—0
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking StudyCode0
Can we predict QPP? An approach based on multivariate outliers—0
Efficient Generation of Hidden Outliers for Improved Outlier DetectionCode0
A Unified Framework for Center-based Clustering of Distributed Data—0
Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and AnalysisCode0
A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and TrackingCode0
Dimensionality-Aware Outlier Detection: Theoretical and Experimental AnalysisCode0
Outlier Ranking in Large-Scale Public Health Streams—0
Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process MixturesCode0
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion ModelsCode1
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time ChangesCode0
User Equipment Assisted Localization for 6G Integrated Sensing and Communication—0
Outlier detection using flexible categorisation and interrogative agendas—0
A Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation—0
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning—0
RANRAC: Robust Neural Scene Representations via Random Ray Consensus—0
Meta-survey on outlier and anomaly detectionCode0
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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