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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 576–600 of 703 papers

TitleStatusHype
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS—0
Can we predict QPP? An approach based on multivariate outliers—0
Cascade Subspace Clustering for Outlier Detection—0
Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection—0
Centering the Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection—0
Implications of Distance over Redistricting Maps: Central and Outlier Maps—0
Characterizing Malicious Edges targeting on Graph Neural Networks—0
Choquet-Based Fuzzy Rough Sets—0
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model—0
Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search—0
Clustering with Outlier Removal—0
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory—0
Cognitive Deep Machine Can Train Itself—0
Combining Structured and Unstructured Randomness in Large Scale PCA—0
Community-based anomaly detection using spectral graph filtering—0
Comparative Study of Neighbor-based Methods for Local Outlier Detection—0
Comparison of Outlier Detection Techniques for Structured Data—0
Comparison of Visual Trackers for Biomechanical Analysis of Running—0
Capturing the Denoising Effect of PCA via Compression Ratio—0
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots—0
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks—0
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation—0
Conditional Testing based on Localized Conformal p-values—0
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Defending Object Detectors against Patch Attacks with Out-of-Distribution Smoothing—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