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 581–590 of 703 papers

Title	Date	Tasks	Status
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier Detection	Apr 26, 2021	Active LearningOutlier Detection	CodeCode Available
Probing Predictions on OOD Images via Nearest Categories	Nov 17, 2020	Adversarial RobustnessFew-Shot Learning	CodeCode Available
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement	Dec 14, 2021	Federated LearningOutlier Detection	CodeCode Available
A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking	Jan 18, 2024	Cell DetectionCell Segmentation	CodeCode Available
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data	Jul 15, 2019	ImputationOutlier Detection	CodeCode Available
Unsupervised Keyphrase Extraction from Scientific Publications	Aug 10, 2018	Keyphrase ExtractionOutlier Detection	CodeCode Available
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples	Jan 28, 2025	DiversityOutlier Detection	CodeCode Available
Anomaly Detection with Selective Dictionary Learning	Jul 17, 2023	Anomaly DetectionDictionary Learning	CodeCode Available
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation	Jun 12, 2020	Anomaly DetectionDensity Ratio Estimation	CodeCode Available
A Probabilistic Transformation of Distance-Based Outliers	May 16, 2023	Anomaly DetectionOutlier Detection	CodeCode Available

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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