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 276–300 of 703 papers

TitleStatusHype
Deep Sequence Modeling for Anomalous ISP Traffic Prediction—0
Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction—0
Improving the Robustness of Federated Learning for Severely Imbalanced Datasets—0
Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks—0
Capturing the Denoising Effect of PCA via Compression Ratio—0
Fluctuation-based Outlier DetectionCode0
Learning to Classify Open Intent via Soft Labeling and Manifold MixupCode0
Anomalous Sound Detection Based on Machine Activity Detection—0
Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version—0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—0
FaceMap: Towards Unsupervised Face Clustering via Map EquationCode1
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profilesCode0
Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding—0
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods—0
Implications of Distance over Redistricting Maps: Central and Outlier Maps—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
Choquet-Based Fuzzy Rough Sets—0
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection—0
Outlier-based Autism Detection using Longitudinal Structural MRI—0
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data—0
Geometry- and Accuracy-Preserving Random Forest ProximitiesCode0
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation—0
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data—0
Adaptive Outlier Detection for Power MOSFETs Based on Gaussian Process Regression—0
Community-based anomaly detection using spectral graph filtering—0
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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