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
MaxGap Bandit: Adaptive Algorithms for Approximate RankingCode0
Adversarially Learned One-Class Classifier for Novelty DetectionCode0
Robust outlier detection by de-biasing VAE likelihoodsCode0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"Code0
EntropyStop: Unsupervised Deep Outlier Detection with Loss EntropyCode0
Further Analysis of Outlier Detection with Deep Generative ModelsCode0
A Fast Greedy Algorithm for Outlier MiningCode0
FairOD: Fairness-aware Outlier DetectionCode0
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence ClassificationCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with OutliersCode0
Automated Generation of Multilingual Clusters for the Evaluation of Distributed RepresentationsCode0
Automatically detecting anomalous exoplanet transitsCode0
A Framework for Clustering Uncertain DataCode0
Distribution and volume based scoring for Isolation ForestsCode0
Automatic support vector data descriptionCode0
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional DataCode0
D.MCA: Outlier Detection with Explicit Micro-Cluster AssignmentsCode0
Edgewise outliers of network indexed signalsCode0
Image Labels Are All You Need for Coarse Seagrass SegmentationCode0
Impact of Comprehensive Data Preprocessing on Predictive Modelling of COVID-19 MortalityCode0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
A Method of Moments Embedding Constraint and its Application to Semi-Supervised LearningCode0
Dimensionality-Aware Outlier Detection: Theoretical and Experimental AnalysisCode0
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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