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 526–550 of 703 papers

TitleStatusHype
Exploring Information Centrality for Intrusion Detection in Large Networks—0
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
Generative Models for Novelty Detection: Applications in abnormal event and situational change detection from data series—0
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems—0
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection—0
Learning Regularity in Skeleton Trajectories for Anomaly Detection in VideosCode0
Explaining Anomalies Detected by Autoencoders Using SHAPCode1
Anomaly Detection for an E-commerce Pricing System—0
Truncated Gaussian-Mixture Variational AutoEncoder—0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"—0
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence ClassificationCode0
PyOD: A Python Toolbox for Scalable Outlier DetectionCode1
Unified Graph based Multi-Cue Feature Fusion for Robust Visual Tracking—0
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in VideoCode0
Robust Ordinal Embedding from Contaminated Relative ComparisonsCode0
LSCP: Locally Selective Combination in Parallel Outlier EnsemblesCode0
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks—0
Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach—0
Outlier Detection using Generative Models with Theoretical Performance Guarantees—0
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph LaplacianCode0
The JHU Parallel Corpus Filtering Systems for WMT 2018—0
The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task—0
Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering—0
STACC, OOV Density and N-gram Saturation: Vicomtech's Participation in the WMT 2018 Shared Task on Parallel Corpus Filtering—0
Generative Adversarial Active Learning for Unsupervised Outlier DetectionCode0
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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