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 421–430 of 703 papers

Title	Date	Tasks	Status
Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates	Sep 3, 2024	Outlier DetectionSynthetic Data Generation	—Unverified
Synthetic outlier generation for anomaly detection in autonomous driving	Aug 4, 2023	Anomaly DetectionAutonomous Driving	—Unverified
Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Method	Feb 25, 2021	ClusteringFraud Detection	—Unverified
Technical outlier detection via convolutional variational autoencoder for the ADMANI breast mammogram dataset	May 20, 2023	Breast Cancer DetectionOutlier Detection	—Unverified
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

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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