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 601–625 of 703 papers

TitleStatusHype
GENDIS: GENetic DIscovery of ShapeletsCode0
Dimensionality-Aware Outlier Detection: Theoretical and Experimental AnalysisCode0
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in VideoCode0
Generative Adversarial Active Learning for Unsupervised Outlier DetectionCode0
TGTOD: A Global Temporal Graph Transformer for Outlier Detection at ScaleCode0
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional DataCode0
ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein EvolutionCode0
Rule-based outlier detection of AI-generated anatomy segmentationsCode0
Geometry- and Accuracy-Preserving Random Forest ProximitiesCode0
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through GradientCode0
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative ModelsCode0
Outlier Detection in Large Radiological Datasets using UMAPCode0
Using Self-Supervised Learning Can Improve Model Robustness and UncertaintyCode0
Weighted Scaling Approach for Metabolomics Data AnalysisCode0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
SDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale DatasetsCode0
Anomaly Detection With Partitioning Overfitting Autoencoder EnsemblesCode0
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning ApproachCode0
PyODDS: An End-to-End Outlier Detection SystemCode0
What Do Compressed Deep Neural Networks Forget?Code0
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of EmotionsCode0
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking StudyCode0
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks SafetyCode0
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed GraphsCode0
Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process MixturesCode0
Show:102550
← PrevPage 25 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