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 211–220 of 703 papers

Title	Date	Tasks	Status
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs	Sep 5, 2023	Lesion SegmentationOutlier Detection	CodeCode Available
Large-scale gradient-based training of Mixtures of Factor Analyzers	Aug 26, 2023	LEMMAOutlier Detection	—Unverified
Quantile-based Maximum Likelihood Training for Outlier Detection	Aug 20, 2023	Autonomous DrivingContrastive Learning	CodeCode Available
Quantifying Outlierness of Funds from their Categories using Supervised Similarity	Aug 14, 2023	ManagementMetric Learning	—Unverified
A Review of Change of Variable Formulas for Generative Modeling	Aug 4, 2023	Bayesian InferenceDecoder	—Unverified
Synthetic outlier generation for anomaly detection in autonomous driving	Aug 4, 2023	Anomaly DetectionAutonomous Driving	—Unverified
Image Outlier Detection Without Training using RANSAC	Jul 23, 2023	One-Class ClassificationOutlier Detection	CodeCode Available
Fast Unsupervised Deep Outlier Model Selection with Hypernetworks	Jul 20, 2023	Meta-Learningmodel	CodeCode Available
Edgewise outliers of network indexed signals	Jul 20, 2023	Outlier Detection	CodeCode Available
Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation	Jul 18, 2023	ClusteringOutlier Detection	CodeCode Available

Show:10 25 50

← PrevPage 22 of 71Next →

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