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
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records DataCode0
Probing Predictions on OOD Images via Nearest CategoriesCode0
Efficient Subspace Search in Data StreamsCode0
Toward Scalable and Unified Example-based Explanation and Outlier Detection—0
Further Analysis of Outlier Detection with Deep Generative ModelsCode0
Autoencoder Watchdog Outlier Detection for Classifiers—0
Rotation Averaging with Attention Graph Neural Networks—0
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation—0
OneFlow: One-class flow for anomaly detection based on a minimal volume region—0
Machine learning based forecasting of significant daily returns in foreign exchange markets—0
Robust Outlier Arm IdentificationCode0
Anomaly Detection With Partitioning Overfitting Autoencoder EnsemblesCode0
Let me join you! Real-time F-formation recognition by a socially aware robot—0
A boosted outlier detection method based on the spectrum of the Laplacian matrix of a graph—0
Outlier detection in non-elliptical data by kernel MRCDCode0
ALRe: Outlier Detection for Guided Refinement—0
Rotational Outlier Identification in Pose Graphs Using Dual Decomposition—0
Byzantine-Resilient Secure Federated Learning—0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
In search of the weirdest galaxies in the UniverseCode0
Data Stream Clustering: A Review—0
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
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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