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 251–300 of 703 papers

TitleStatusHype
AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder—0
Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing—0
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data—0
LEARNING SEMANTIC WORD RESPRESENTATIONS VIA TENSOR FACTORIZATION—0
Evaluating the Efficacy of Foundational Models: Advancing Benchmarking Practices to Enhance Fine-Tuning Decision-Making—0
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles—0
HLoOP -- Hyperbolic 2-space Local Outlier Probabilities—0
A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques—0
Holistic Features For Real-Time Crowd Behaviour Anomaly Detection—0
Hyperbolic Metric Learning for Visual Outlier Detection—0
Choquet-Based Fuzzy Rough Sets—0
Characterizing Malicious Edges targeting on Graph Neural Networks—0
Anomaly Detection with HMM Gauge Likelihood Analysis—0
Implications of Distance over Redistricting Maps: Central and Outlier Maps—0
Centering the Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection—0
A Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation—0
Highly Efficient Direct Analytics on Semantic-aware Time Series Data Compression—0
Variational Hyper-Encoding Networks—0
Image Modeling with Deep Convolutional Gaussian Mixture Models—0
Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection—0
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data—0
HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI—0
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection—0
Anomaly Detection using Capsule Networks for High-dimensional Datasets—0
Fairness-aware Outlier Ensemble—0
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images—0
GWQ: Gradient-Aware Weight Quantization for Large Language Models—0
Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest—0
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS—0
Exploring Outliers in Crowdsourced Ranking for QoE—0
Fair Outlier Detection—0
Can we predict QPP? An approach based on multivariate outliers—0
Cascade Subspace Clustering for Outlier Detection—0
Feature Engineering for Scalable Application-Level Post-Silicon Debugging—0
Feature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging—0
FedCC: Robust Federated Learning against Model Poisoning Attacks—0
Exploring Information Centrality for Intrusion Detection in Large Networks—0
Female mosquito detection by means of AI techniques inside release containers in the context of a Sterile Insect Technique program—0
Finding Inner Outliers in High Dimensional Space—0
Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering—0
Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations—0
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data—0
Outlier detection using flexible categorisation and interrogative agendas—0
Flexible categorization using formal concept analysis and Dempster-Shafer theory—0
FlexUOD: The Answer to Real-world Unsupervised Image Outlier Detection—0
OneFlow: One-class flow for anomaly detection based on a minimal volume region—0
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model—0
C-AllOut: Catching & Calling Outliers by Type—0
Homophily Outlier Detection in Non-IID Categorical Data—0
Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding—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