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 351–375 of 703 papers

TitleStatusHype
SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection—0
SSDBCODI: Semi-Supervised Density-Based Clustering with Outliers Detection Integrated—0
STACC, OOV Density and N-gram Saturation: Vicomtech's Participation in the WMT 2018 Shared Task on Parallel Corpus Filtering—0
Statistical Outlier Identification in Multi-robot Visual SLAM using Expectation Maximization—0
Structured Group Sparsity: A Novel Indoor WLAN Localization, Outlier Detection, and Radio Map Interpolation Scheme—0
Suppressing Outlier Reconstruction in Autoencoders for Out-of-Distribution Detection—0
Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates—0
Synthetic outlier generation for anomaly detection in autonomous driving—0
Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Method—0
Technical outlier detection via convolutional variational autoencoder for the ADMANI breast mammogram dataset—0
Temporal Analysis of Adversarial Attacks in Federated Learning—0
That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering—0
The Clever Hans Effect in Anomaly Detection—0
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods—0
The Geometry of Nodal Sets and Outlier Detection—0
The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task—0
ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets—0
The Invariant Ground Truth of Affect—0
The JHU Parallel Corpus Filtering Systems for WMT 2018—0
Tight Rates in Supervised Outlier Transfer Learning—0
Towards a Model for LSH—0
Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction—0
Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness—0
Towards Building Affect sensitive Word Distributions—0
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction—0
Show:102550
← PrevPage 15 of 29Next →
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