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 376–400 of 703 papers

TitleStatusHype
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks—0
ast2vec: Utilizing Recursive Neural Encodings of Python ProgramsCode0
SSD: A Unified Framework for Self-Supervised Outlier DetectionCode1
Homophily Outlier Detection in Non-IID Categorical Data—0
Fairness-aware Outlier Ensemble—0
Impacts of the Numbers of Colors and Shapes on Outlier Detection: from Automated to User Evaluation—0
Symbiotic Hybrid Neural Network Watchdog For Outlier Detection—0
Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Method—0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
DEUP: Direct Epistemic Uncertainty PredictionCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Feature Engineering for Scalable Application-Level Post-Silicon Debugging—0
RECol: Reconstruction Error Columns for Outlier Detection—0
Measuring Dependence with Matrix-based Entropy FunctionalCode0
Dense outlier detection and open-set recognition based on training with noisy negative images—0
Enhancing Visual Representations for Efficient Object Recognition during Online Distillation—0
Suppressing Outlier Reconstruction in Autoencoders for Out-of-Distribution Detection—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines—0
Outlier Preserving Distribution Mapping Autoencoders—0
Outlier Robust Optimal Transport—0
Multidimensional Uncertainty-Aware Evidential Neural NetworksCode1
On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New InsightsCode1
Probabilistic Outlier Detection and Generation—0
Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments—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