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

Showing 101125 of 703 papers

TitleStatusHype
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time ChangesCode0
GENDIS: GENetic DIscovery of ShapeletsCode0
ast2vec: Utilizing Recursive Neural Encodings of Python ProgramsCode0
FairOD: Fairness-aware Outlier DetectionCode0
Fast Incremental SVDD Learning Algorithm with the Gaussian KernelCode0
Learning to Classify Open Intent via Soft Labeling and Manifold MixupCode0
Fluctuation-based Outlier DetectionCode0
Edgewise outliers of network indexed signalsCode0
Further Analysis of Outlier Detection with Deep Generative ModelsCode0
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative NetworkCode0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
Automated Generation of Multilingual Clusters for the Evaluation of Distributed RepresentationsCode0
Automatically detecting anomalous exoplanet transitsCode0
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through GradientCode0
D.MCA: Outlier Detection with Explicit Micro-Cluster AssignmentsCode0
Automatic support vector data descriptionCode0
Efficient Curation of Invertebrate Image Datasets Using Feature Embeddings and Automatic Size ComparisonCode0
Dimensionality-Aware Outlier Detection: Theoretical and Experimental AnalysisCode0
A Method of Moments Embedding Constraint and its Application to Semi-Supervised LearningCode0
Distribution and volume based scoring for Isolation ForestsCode0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
A Radiometric Correction based Optical Modeling Approach to Removing Reflection Noise in TLS Point Clouds of Urban ScenesCode0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with OutliersCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98Unverified
2F-t ALSTM-FCNAccuracy0.95Unverified
3GENDISAccuracy0.94Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74Unverified