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 631–640 of 703 papers

Title	Date	Tasks	Status
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection	Feb 8, 2018	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available
Image Labels Are All You Need for Coarse Seagrass Segmentation	Mar 2, 2023	AllOutlier Detection	CodeCode Available
Adversarially Learned One-Class Classifier for Novelty Detection	Feb 25, 2018	Anomaly DetectionNovelty Detection	CodeCode Available
Impact of Comprehensive Data Preprocessing on Predictive Modelling of COVID-19 Mortality	Aug 15, 2024	feature selectionOutlier Detection	CodeCode Available
Image Outlier Detection Without Training using RANSAC	Jul 23, 2023	One-Class ClassificationOutlier Detection	CodeCode Available
Importance Sampling for Nonlinear Models	May 18, 2025	Outlier Detection	CodeCode Available
MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction	Apr 3, 2023	Outlier Detection	CodeCode Available
Understanding and Mitigating the Effect of Outliers in Fair Ranking	Dec 21, 2021	FairnessOutlier Detection	CodeCode Available
The magnitude vector of images	Oct 28, 2021	Boundary DetectionDescriptive	CodeCode Available
Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++	Aug 29, 2022	Outlier 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