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 361–370 of 703 papers

Title	Date	Tasks	Status
Temporal Analysis of Adversarial Attacks in Federated Learning	Jan 19, 2025	Federated LearningOutlier Detection	—Unverified
That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering	Jul 6, 2023	Anomaly DetectionClustering	—Unverified
The Clever Hans Effect in Anomaly Detection	Jun 18, 2020	Anomaly DetectionExplainable Artificial Intelligence (XAI)	—Unverified
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods	Mar 4, 2022	Anomaly DetectionObject	—Unverified
The Geometry of Nodal Sets and Outlier Detection	Jun 5, 2017	Outlier Detection	—Unverified
The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task	Oct 1, 2018	ARCClustering	—Unverified
ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets	Nov 6, 2022	Outlier Detectionregression	—Unverified
The Invariant Ground Truth of Affect	Oct 14, 2022	Outlier Detection	—Unverified
The JHU Parallel Corpus Filtering Systems for WMT 2018	Oct 1, 2018	Language ModelingLanguage Modelling	—Unverified
Tight Rates in Supervised Outlier Transfer Learning	Oct 7, 2023	Outlier DetectionTransfer Learning	—Unverified

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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