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 151–200 of 703 papers

TitleStatusHype
Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments—0
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability—0
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition—0
A Local Density-Based Approach for Local Outlier Detection—0
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks—0
Comparative Study of Neighbor-based Methods for Local Outlier Detection—0
Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions—0
Community-based anomaly detection using spectral graph filtering—0
Combining Structured and Unstructured Randomness in Large Scale PCA—0
An Outlier Detection-based Tree Selection Approach to Extreme Pruning of Random Forests—0
About Test-time training for outlier detection—0
Comparison of Outlier Detection Techniques for Structured Data—0
Comparison of Visual Trackers for Biomechanical Analysis of Running—0
Capturing the Denoising Effect of PCA via Compression Ratio—0
Detect Professional Malicious User with Metric Learning in Recommender Systems—0
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots—0
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks—0
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation—0
Conditional Testing based on Localized Conformal p-values—0
A Practical Algorithm for Distributed Clustering and Outlier Detection—0
A probabilistic view on Riemannian machine learning models for SPD matrices—0
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Diffusion Nets—0
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks—0
Defending Object Detectors against Patch Attacks with Out-of-Distribution Smoothing—0
Contextual Outlier Interpretation—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
Contextual Unsupervised Outlier Detection in Sequences—0
Continual Learning with Fully Probabilistic Models—0
Cognitive Deep Machine Can Train Itself—0
Coverage-based Outlier Explanation—0
Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach—0
Cross Domain Image Matching in Presence of Outliers—0
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)—0
A Review of Change of Variable Formulas for Generative Modeling—0
A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks—0
Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
Data Stream Clustering: A Review—0
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory—0
Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control—0
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes—0
A Non-Parametric Control Chart For High Frequency Multivariate Data—0
A boosted outlier detection method based on the spectrum of the Laplacian matrix of a graph—0
Deep Learning for Anomaly Detection: A Review—0
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments—0
Deep Learning with Sets and Point Clouds—0
A Robust Regression Approach for Robot Model Learning—0
Detecting Surprising Situations in Event Data—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