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
Hyperparameter Optimization for Unsupervised Outlier Detection—0
Traffic congestion anomaly detection and prediction using deep learning—0
Transfer Neyman-Pearson Algorithm for Outlier Detection—0
Transformation Autoregressive Networks—0
TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER—0
Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning—0
tsrobprep - an R package for robust preprocessing of time series data—0
Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System—0
Uncertainty in Supply Chain Digital Twins: A Quantum-Classical Hybrid Approach—0
Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning—0
Unified Graph based Multi-Cue Feature Fusion for Robust Visual Tracking—0
Universal Embeddings of Tabular Data—0
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series—0
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics—0
Unsupervised Event Outlier Detection in Continuous Time—0
Unsupervised outlier detection to improve bird audio dataset labels—0
Unsupervised Outlier Detection using Memory and Contrastive Learning—0
Unsupervised Parameter-free Outlier Detection using HDBSCAN* Outlier Profiles—0
Unsupervised routine discovery in egocentric photo-streams—0
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version—0
User Equipment Assisted Localization for 6G Integrated Sensing and Communication—0
Using Eigencentrality to Estimate Joint, Conditional and Marginal Probabilities from Mixed-Variable Data: Method and Applications—0
Using Images to Find Context-Independent Word Representations in Vector Space—0
Validating and Exploring Large Geographic Corpora—0
Truncated Gaussian-Mixture Variational AutoEncoder—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