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 401–410 of 703 papers

Title	Date	Tasks	Status
Versatile Anomaly Detection with Outlier Preserving Distribution Mapping Autoencoders	Sep 25, 2019	Anomaly DetectionOutlier Detection	—Unverified
Visualizing and Exploring Dynamic High-Dimensional Datasets with LION-tSNE	Aug 16, 2017	Dimensionality ReductionOutlier Detection	—Unverified
Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection	Jun 1, 2021	BIG-bench Machine LearningBoundary Detection	—Unverified
WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining	Jun 9, 2023	Fault DetectionNovelty Detection	—Unverified
What do we learn? Debunking the Myth of Unsupervised Outlier Detection	Jun 8, 2022	Outlier DetectionOut of Distribution (OOD) Detection	—Unverified
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks	Aug 8, 2019	Anomaly DetectionGenerative Adversarial Network	—Unverified
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?	Sep 29, 2021	Home Activity MonitoringOutlier Detection	—Unverified
Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!	Sep 9, 2024	Model SelectionOutlier Detection	—Unverified
Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing	May 12, 2023	compressed sensingEfficient Neural Network	—Unverified
Robust outlier detection by de-biasing VAE likelihoods	Aug 19, 2021	DecoderOutlier Detection	—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