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 501–550 of 703 papers

TitleStatusHype
LOSDD: Leave-Out Support Vector Data Description for Outlier Detection—0
Lower Dimensional Spherical Representation of Medium Voltage Load Profiles for Visualization, Outlier Detection, and Generative Modelling—0
LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis—0
Machine learning based forecasting of significant daily returns in foreign exchange markets—0
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method—0
Mathematical Models for Local Sensing Hashes—0
MCODE: Multivariate Conditional Outlier Detection—0
Meta-learning for Positive-unlabeled Classification—0
Meta-Learning for Relative Density-Ratio Estimation—0
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport—0
Meta-Unsupervised-Learning: A supervised approach to unsupervised learning—0
Minimal Solutions for Relative Pose with a Single Affine Correspondence—0
M5 -- A Diverse Benchmark to Assess the Performance of Large Multimodal Models Across Multilingual and Multicultural Vision-Language Tasks—0
Model-based Exception Mining for Object-Relational Data—0
Motion Estimation via Robust Decomposition with Constrained Rank—0
MSD-Kmeans: A Novel Algorithm for Efficient Detection of Global and Local Outliers—0
Multimodal Foundational Models for Unsupervised 3D General Obstacle Detection—0
Multiple testing for outlier detection in functional data—0
Multivariate outlier detection based on a robust Mahalanobis distance with shrinkage estimators—0
Network Volume Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking—0
NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations—0
News-based Business Sentiment and its Properties as an Economic Index—0
Noisy Subspace Clustering via Thresholding—0
Nonconvex Approach for Sparse and Low-Rank Constrained Models with Dual Momentum—0
Non-parametric cumulants approach for outlier detection of multivariate financial data—0
Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization—0
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning—0
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches—0
One-Class Kernel Spectral Regression—0
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities—0
On Integrated Clustering and Outlier Detection—0
Onion-Peeling Outlier Detection in 2-D data Sets—0
On Language Clustering: A Non-parametric Statistical Approach—0
On Soft Power Diagrams—0
On the Adversarial Robustness of Benjamini Hochberg—0
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations—0
Optimization of Retrieval-Augmented Generation Context with Outlier Detection—0
Ordinal time series analysis with the R package otsfeatures—0
OuroMamba: A Data-Free Quantization Framework for Vision Mamba Models—0
OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets—0
Outlier-based Autism Detection using Longitudinal Structural MRI—0
Outlier Cluster Formation in Spectral Clustering—0
Outlier Detection and Data Clustering via Innovation Search—0
Outlier Detection and Robust PCA Using a Convex Measure of Innovation—0
Outlier Detection and Spatial Analysis Algorithms—0
Outlier Detection as Instance Selection Method for Feature Selection in Time Series Classification—0
Efficient Generation of Hidden Outliers for Improved Outlier DetectionCode0
ast2vec: Utilizing Recursive Neural Encodings of Python ProgramsCode0
Out-of-Scope Intent Detection with Self-Supervision and Discriminative TrainingCode0
Efficient Subspace Search in Data StreamsCode0
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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