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 641–650 of 703 papers

Title	Date	Tasks	Status
Depth-Based Object Tracking Using a Robust Gaussian Filter	Feb 19, 2016	Computational EfficiencyObject	CodeCode Available
In search of the weirdest galaxies in the Universe	Jul 16, 2020	ClusteringDimensionality Reduction	CodeCode Available
Variational Autoencoders for Anomalous Jet Tagging	Jul 3, 2020	Jet TaggingOutlier Detection	CodeCode Available
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Description	Jul 20, 2020	Community DetectionGraph Embedding	CodeCode Available
Dense open-set recognition with synthetic outliers generated by Real NVP	Nov 22, 2020	Autonomous Drivingimage-classification	CodeCode Available
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data	Nov 18, 2020	Decision MakingICU Admission	CodeCode Available
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning	Apr 9, 2024	Federated LearningNetwork Pruning	CodeCode Available
Automatic support vector data description	Jan 1, 2018	One-class classifierOutlier Detection	CodeCode Available
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis	Sep 29, 2022	Dimensionality ReductionOutlier Detection	CodeCode Available
Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift	Aug 3, 2019	Anomaly DetectionDiversity	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