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 361–370 of 703 papers

Title	Date	Tasks	Status	Hype
Unsupervised Offline Changepoint Detection Ensembles	May 9, 2021	Anomaly DetectionChange Point Detection	CodeCode Available	1
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder	May 5, 2021	Autonomous VehiclesNovelty Detection	CodeCode Available	0
ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals	Apr 29, 2021	BIG-bench Machine Learningfeature selection	—Unverified	0
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation	Apr 28, 2021	Gaussian ProcessesImage Segmentation	—Unverified	0
tsrobprep - an R package for robust preprocessing of time series data	Apr 26, 2021	ClusteringImputation	—Unverified	0
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier Detection	Apr 26, 2021	Active LearningOutlier Detection	CodeCode Available	0
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation	Apr 22, 2021	Outlier Detectionregression	—Unverified	0
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network	Apr 19, 2021	Anomaly DetectionOutlier Detection	CodeCode Available	1
Image Modeling with Deep Convolutional Gaussian Mixture Models	Apr 19, 2021	ClusteringOutlier Detection	—Unverified	0
Continual Learning with Fully Probabilistic Models	Apr 19, 2021	Boundary Detectionclass-incremental learning	—Unverified	0

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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