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 426–450 of 703 papers

TitleStatusHype
ALRe: Outlier Detection for Guided Refinement—0
Handcrafted Outlier Detection RevisitedCode1
Byzantine-Resilient Secure Federated Learning—0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
Data Stream Clustering: A Review—0
In search of the weirdest galaxies in the UniverseCode0
Generic Outlier Detection in Multi-Armed Bandit—0
It Is Likely That Your Loss Should be a Likelihood—0
Learning low-dimensional manifolds under the L0-norm constraint for unsupervised outlier detection—0
Deep Learning for Anomaly Detection: A Review—0
Variational Autoencoders for Anomalous Jet TaggingCode0
Explainable Deep One-Class ClassificationCode1
Outlier Detection through Null Space Analysis of Neural Networks—0
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent ClassificationCode1
Practical applications of metric space magnitude and weighting vectors—0
Traffic congestion anomaly detection and prediction using deep learning—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
The Clever Hans Effect in Anomaly Detection—0
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction—0
SDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale DatasetsCode0
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio EstimationCode0
Probabilistic AutoencoderCode1
Outlier Detection Using a Novel method: Quantum Clustering—0
Picket: Guarding Against Corrupted Data in Tabular Data during Learning and InferenceCode1
AdaLAM: Revisiting Handcrafted Outlier DetectionCode1
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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