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
Robust Conformal Outlier Detection under Contaminated Reference DataCode0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution SamplesCode0
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks—0
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model—0
Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders—0
Temporal Analysis of Adversarial Attacks in Federated Learning—0
Outlyingness Scores with Cluster Catch Digraphs—0
On the Adversarial Robustness of Benjamini Hochberg—0
Transfer Neyman-Pearson Algorithm for Outlier Detection—0
An Efficient Outlier Detection Algorithm for Data Streaming—0
FlexUOD: The Answer to Real-world Unsupervised Image Outlier Detection—0
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing—0
Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive ModelsCode0
Brain Ageing Prediction using Isolation Forest Technique and Residual Neural Network (ResNet)—0
Efficient Curation of Invertebrate Image Datasets Using Feature Embeddings and Automatic Size ComparisonCode0
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through GradientCode0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
Detecting outliers by clustering algorithms—0
Backdooring Outlier Detection Methods: A Novel Attack Approach—0
Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions—0
TGTOD: A Global Temporal Graph Transformer for Outlier Detection at ScaleCode0
Using Images to Find Context-Independent Word Representations in Vector Space—0
Unsupervised Event Outlier Detection in Continuous Time—0
Lower Dimensional Spherical Representation of Medium Voltage Load Profiles for Visualization, Outlier Detection, and Generative Modelling—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