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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 221–230 of 703 papers

Title	Date	Tasks	Status	Hype
FedCC: Robust Federated Learning against Model Poisoning Attacks	Dec 5, 2022	Federated Learningmodel	—Unverified	0
RbA: Segmenting Unknown Regions Rejected by All	Nov 25, 2022	AllAnomaly Detection	CodeCode Available	1
A Deep Learning Anomaly Detection Method in Textual Data	Nov 25, 2022	Anomaly DetectionDeep Learning	—Unverified	0
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD	Nov 23, 2022	Anomaly DetectionOutlier Detection	CodeCode Available	0
PNI : Industrial Anomaly Detection using Position and Neighborhood Information	Nov 22, 2022	Anomaly DetectionOutlier Detection	CodeCode Available	1
OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language	Nov 8, 2022	Outlier Detection	CodeCode Available	1
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction	Nov 8, 2022	Active LearningOutlier Detection	—Unverified	0
ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets	Nov 6, 2022	Outlier Detectionregression	—Unverified	0
Toward Unsupervised Outlier Model Selection	Nov 3, 2022	Meta-Learningmodel	CodeCode Available	1
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport	Nov 1, 2022	Meta-LearningOutlier Detection	—Unverified	0

Show:10 25 50

← PrevPage 23 of 71Next →

All datasets ECG5000 Balance scale_class 1 Breast cancer Wisconsin_class 2 Breast cancer Wisconsin_class 4 Fashion-MNIST Glass identification Heart-C Hepatitis Internet Ad Ionosphere_class b SKAB

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	VRAE+SVM	Accuracy	0.98	—	Unverified
2	F-t ALSTM-FCN	Accuracy	0.95	—	Unverified
3	GENDIS	Accuracy	0.94	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	99.03	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	37.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	65.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PAE	AUROC	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	99.05	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC	0.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC-ROC	0.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC-ROC	0.93	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	86.33	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LSTMCaps	Average F1	0.74	—	Unverified