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 176–200 of 703 papers

TitleStatusHype
Defending Object Detectors against Patch Attacks with Out-of-Distribution Smoothing—0
Contextual Outlier Interpretation—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
Contextual Unsupervised Outlier Detection in Sequences—0
Continual Learning with Fully Probabilistic Models—0
AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks—0
Coverage-based Outlier Explanation—0
Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach—0
Cross Domain Image Matching in Presence of Outliers—0
Cognitive Deep Machine Can Train Itself—0
A Review of Change of Variable Formulas for Generative Modeling—0
A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks—0
Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
Data Stream Clustering: A Review—0
Detecting Outliers in Data with Correlated Measures—0
Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control—0
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes—0
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory—0
A Non-Parametric Control Chart For High Frequency Multivariate Data—0
Deep Learning for Anomaly Detection: A Review—0
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments—0
Deep Learning with Sets and Point Clouds—0
A Robust Regression Approach for Robot Model Learning—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—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