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 126–150 of 703 papers

TitleStatusHype
Uncertainty in Supply Chain Digital Twins: A Quantum-Classical Hybrid Approach—0
Unsupervised Parameter-free Outlier Detection using HDBSCAN* Outlier Profiles—0
Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKACode0
GWQ: Gradient-Aware Weight Quantization for Large Language Models—0
Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak RebatesCode0
A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks—0
Lifted Coefficient of Determination: Fast model-free prediction intervals and likelihood-free model comparison—0
ECORS: An Ensembled Clustering Approach to Eradicate The Local And Global Outlier In Collaborative Filtering Recommender System—0
Conditional Testing based on Localized Conformal p-values—0
Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control—0
Outlier Detection with Cluster Catch Digraphs—0
Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!—0
Interpreting Outliers in Time Series Data through Decoding Autoencoder—0
Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates—0
Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers (Extended Version)—0
Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors—0
Flexible categorization using formal concept analysis and Dempster-Shafer theory—0
Multimodal Foundational Models for Unsupervised 3D General Obstacle Detection—0
ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect—0
Impact of Comprehensive Data Preprocessing on Predictive Modelling of COVID-19 MortalityCode0
EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier LogitsCode0
Regularized Contrastive Partial Multi-view Outlier Detection—0
Outlier Detection in Large Radiological Datasets using UMAPCode0
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning—0
Rethinking Unsupervised Outlier Detection via Multiple ThresholdingCode0
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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