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 251–275 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
Robust outlier detection by de-biasing VAE likelihoods—0
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data—0
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data—0
Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations—0
Outlier detection using flexible categorisation and interrogative agendas—0
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model—0
A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques—0
Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering—0
Flexible categorization using formal concept analysis and Dempster-Shafer theory—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
FlexUOD: The Answer to Real-world Unsupervised Image Outlier Detection—0
GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting—0
Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection—0
Cascade Subspace Clustering for Outlier Detection—0
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data—0
Can we predict QPP? An approach based on multivariate outliers—0
Anomaly Detection using Capsule Networks for High-dimensional Datasets—0
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images—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