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 471–480 of 703 papers

Title	Date	Tasks	Status
Outlier Detection and Data Clustering via Innovation Search	Dec 30, 2019	ClusteringOutlier Detection	—Unverified
Minimal Solutions for Relative Pose with a Single Affine Correspondence	Dec 23, 2019	Motion EstimationOutlier Detection	—Unverified
Event Outlier Detection in Continuous Time	Dec 19, 2019	Outlier DetectionPoint Processes	CodeCode Available
Active Learning of SVDD Hyperparameter Values	Dec 4, 2019	Active LearningOutlier Detection	—Unverified
XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning	Dec 1, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network	Dec 1, 2019	Outlier DetectionRepresentation Learning	CodeCode Available
Outlier Detection and Robust PCA Using a Convex Measure of Innovation	Dec 1, 2019	Outlier Detection	—Unverified
Multivariate outlier detection based on a robust Mahalanobis distance with shrinkage estimators	Nov 20, 2019	Outlier Detection	—Unverified
NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations	Nov 18, 2019	Backdoor AttackOutlier Detection	—Unverified
Walking the Tightrope: An Investigation of the Convolutional Autoencoder Bottleneck	Nov 18, 2019	Outlier DetectionRepresentation Learning	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