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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 426–450 of 703 papers

TitleStatusHype
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
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
GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting—0
GBG++: A Fast and Stable Granular Ball Generation Method for Classification—0
Generating Artificial Outliers in the Absence of Genuine Ones -- a Survey—0
Generative Models for Novelty Detection: Applications in abnormal event and situational change detection from data series—0
Generic Outlier Detection in Multi-Armed Bandit—0
Geometric Tight Frame based Stylometry for Art Authentication of van Gogh Paintings—0
Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)—0
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection—0
GWQ: Gradient-Aware Weight Quantization for Large Language Models—0
HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI—0
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection—0
Hierarchical Multiresolution Feature- and Prior-based Graphs for Classification—0
Highly Efficient Direct Analytics on Semantic-aware Time Series Data Compression—0
HLoOP -- Hyperbolic 2-space Local Outlier Probabilities—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