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 476–500 of 703 papers

TitleStatusHype
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative NetworkCode0
Outlier Detection and Robust PCA Using a Convex Measure of Innovation—0
Multivariate outlier detection based on a robust Mahalanobis distance with shrinkage estimators—0
NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations—0
Walking the Tightrope: An Investigation of the Convolutional Autoencoder BottleneckCode0
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy—0
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets—0
What Do Compressed Deep Neural Networks Forget?Code0
Deep Variational Semi-Supervised Novelty Detection—0
Coverage-based Outlier Explanation—0
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)—0
A Model for Spatial Outlier Detection Based on Weighted Neighborhood Relationship—0
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type DataCode0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
Distance approximation using Isolation Forests—0
DRGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images—0
Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning—0
Unsupervised Boosting-based Autoencoder Ensembles for Outlier DetectionCode0
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score MatchingCode1
MSD-Kmeans: A Novel Algorithm for Efficient Detection of Global and Local Outliers—0
Estimating Density Models with Truncation Boundaries using Score MatchingCode0
PyODDS: An End-to-End Outlier Detection SystemCode0
Versatile Anomaly Detection with Outlier Preserving Distribution Mapping Autoencoders—0
Annealed Denoising score matching: learning Energy based model in high-dimensional spaces—0
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments—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