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 276–300 of 703 papers

TitleStatusHype
Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)—0
Anomaly Detection using Capsule Networks for High-dimensional Datasets—0
Fairness-aware Outlier Ensemble—0
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images—0
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection—0
HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI—0
Can we predict QPP? An approach based on multivariate outliers—0
Cascade Subspace Clustering for Outlier Detection—0
Feature Engineering for Scalable Application-Level Post-Silicon Debugging—0
Feature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging—0
FedCC: Robust Federated Learning against Model Poisoning Attacks—0
Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest—0
Female mosquito detection by means of AI techniques inside release containers in the context of a Sterile Insect Technique program—0
Finding Inner Outliers in High Dimensional Space—0
Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering—0
Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations—0
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data—0
Outlier detection using flexible categorisation and interrogative agendas—0
Flexible categorization using formal concept analysis and Dempster-Shafer theory—0
FlexUOD: The Answer to Real-world Unsupervised Image Outlier Detection—0
OneFlow: One-class flow for anomaly detection based on a minimal volume region—0
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model—0
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS—0
Holistic Features For Real-Time Crowd Behaviour Anomaly Detection—0
Exploring Outliers in Crowdsourced Ranking for QoE—0
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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