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	Hype
Real-Time Outlier Detection with Dynamic Process Limits	Jan 31, 2023	Anomaly DetectionOutlier Detection	—Unverified	0
Conformal inference is (almost) free for neural networks trained with early stopping	Jan 27, 2023	Multi-class ClassificationOutlier Detection	CodeCode Available	0
Open-Set Likelihood Maximization for Few-Shot Learning	Jan 20, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models	Jan 11, 2023	MemorizationOutlier Detection	CodeCode Available	0
A Meta-Learning Algorithm for Interrogative Agendas	Jan 4, 2023	Meta-LearningOutlier Detection	—Unverified	0
LOSDD: Leave-Out Support Vector Data Description for Outlier Detection	Dec 27, 2022	One-Class ClassificationOutlier Detection	—Unverified	0
EDoG: Adversarial Edge Detection For Graph Neural Networks	Dec 27, 2022	Drug DesignEdge Detection	—Unverified	0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection	Dec 15, 2022	Anomaly DetectionIntrusion Detection	—Unverified	0
Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation	Dec 15, 2022	Camera CalibrationKeypoint Detection	—Unverified	0
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree	Dec 13, 2022	ClusteringOutlier Detection	—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