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 661–670 of 703 papers

Title	Date	Tasks	Status
Road User Abnormal Trajectory Detection using a Deep Autoencoder	Aug 25, 2018	BIG-bench Machine LearningOutlier Detection	CodeCode Available
Deep Semi-Supervised Anomaly Detection	Jun 6, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier Logits	Aug 4, 2024	Few-Shot LearningOpen Set Learning	CodeCode Available
Automatically detecting anomalous exoplanet transits	Nov 16, 2021	Outlier Detection	CodeCode Available
Outlier detection in non-elliptical data by kernel MRCD	Aug 5, 2020	Outlier Detection	CodeCode Available
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian	Oct 25, 2018	Anomaly DetectionClustering	CodeCode Available
Deep One-Class Classification	Jul 1, 2018	Anomaly DetectionClassification	CodeCode Available
Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations	Nov 4, 2016	Outlier DetectionSentiment Analysis	CodeCode Available
Outlier detection of vital sign trajectories from COVID-19 patients	Jul 15, 2022	Outlier DetectionTime Series	CodeCode Available
Outlier Detection on Mixed-Type Data: An Energy-based Approach	Aug 17, 2016	Outlier DetectionVocal Bursts Type Prediction	CodeCode Available

Show:10 25 50

← PrevPage 67 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