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 451–500 of 703 papers

TitleStatusHype
Variational Autoencoders for Anomalous Jet TaggingCode0
Outlier Detection through Null Space Analysis of Neural Networks—0
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
Outlier Detection Using a Novel method: Quantum Clustering—0
Generating Artificial Outliers in the Absence of Genuine Ones -- a Survey—0
Outlier Detection in Indoor Localization and Internet of Things (IoT) using Machine Learning—0
Component-wise Adaptive Trimming For Robust Mixture Regression—0
Consistent and Flexible Selectivity Estimation for High-Dimensional DataCode0
Fair Outlier Detection—0
Variational Hyper-Encoding Networks—0
Adaptive Double-Exploration Tradeoff for Outlier Detection—0
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations—0
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series—0
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data—0
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning—0
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection—0
RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies—0
A review on outlier/anomaly detection in time series data—0
Statistical Outlier Identification in Multi-robot Visual SLAM using Expectation Maximization—0
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks—0
Outlier Detection Ensemble with Embedded Feature Selection—0
Outlier Detection and Data Clustering via Innovation Search—0
Minimal Solutions for Relative Pose with a Single Affine Correspondence—0
Event Outlier Detection in Continuous TimeCode0
Active Learning of SVDD Hyperparameter Values—0
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
XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation LearningCode0
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
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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