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 571–580 of 703 papers

Title	Date	Tasks	Status
Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning	Jul 7, 2023	Active LearningOutlier Detection	CodeCode Available
HPSCAN: Human Perception-Based Scattered Data Clustering	Apr 27, 2023	ClusteringOutlier Detection	CodeCode Available
Walking the Tightrope: An Investigation of the Convolutional Autoencoder Bottleneck	Nov 18, 2019	Outlier DetectionRepresentation Learning	CodeCode Available
D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments	Oct 15, 2022	ClusteringOutlier Detection	CodeCode Available
Multi-Person Pose Estimation with Local Joint-to-Person Associations	Aug 30, 2016	Keypoint DetectionMulti-Person Pose Estimation	CodeCode Available
FairOD: Fairness-aware Outlier Detection	Dec 5, 2020	FairnessOutlier Detection	CodeCode Available
CrowdGuard: Federated Backdoor Detection in Federated Learning	Oct 14, 2022	Federated LearningOutlier Detection	CodeCode Available
Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel	Sep 1, 2017	Incremental LearningOutlier Detection	CodeCode Available
Fast Unsupervised Deep Outlier Model Selection with Hypernetworks	Jul 20, 2023	Meta-Learningmodel	CodeCode Available
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers	Aug 14, 2018	Active LearningGeneral Classification	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