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 131–140 of 703 papers

Title	Date	Tasks	Status	Score
D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments	Oct 15, 2022	ClusteringOutlier Detection	CodeCode Available	5
A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking	Jan 18, 2024	Cell DetectionCell Segmentation	CodeCode Available	5
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles	Mar 13, 2022	BenchmarkingBIG-bench Machine Learning	CodeCode Available	5
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers	May 30, 2025	Outlier Detection	CodeCode Available	5
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?	Nov 9, 2023	Meta-LearningModel Selection	CodeCode Available	5
Efficient variational Bayesian neural network ensembles for outlier detection	Mar 20, 2017	Outlier Detection	CodeCode Available	5
Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel	Sep 1, 2017	Incremental LearningOutlier Detection	CodeCode Available	5
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection	Feb 8, 2018	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	5
Outlier Detection in Large Radiological Datasets using UMAP	Jul 31, 2024	Outlier Detection	CodeCode Available	5
Dense open-set recognition with synthetic outliers generated by Real NVP	Nov 22, 2020	Autonomous Drivingimage-classification	CodeCode Available	5

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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