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 331–340 of 703 papers

Title	Date	Tasks	Status
A Deep Learning Anomaly Detection Method in Textual Data	Nov 25, 2022	Anomaly DetectionDeep Learning	—Unverified
Coverage-based Outlier Explanation	Nov 6, 2019	Outlier Detection	—Unverified
Are Outlier Detection Methods Resilient to Sampling?	Jul 31, 2019	Outlier Detection	—Unverified
Continual Learning with Fully Probabilistic Models	Apr 19, 2021	Boundary Detectionclass-incremental learning	—Unverified
Contextual Unsupervised Outlier Detection in Sequences	Nov 6, 2021	Outlier Detection	—Unverified
A refined convergence analysis of pDCA_e with applications to simultaneous sparse recovery and outlier detection	Apr 19, 2018	compressed sensingCPU	—Unverified
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model	Mar 14, 2025	Bayesian InferenceEfficient Exploration	—Unverified
Contextual Outlier Interpretation	Nov 28, 2017	feature selectionOutlier Detection	—Unverified
Defending Object Detectors against Patch Attacks with Out-of-Distribution Smoothing	May 18, 2022	BIG-bench Machine Learningobject-detection	—Unverified
A Rank-Based Similarity Metric for Word Embeddings	May 4, 2018	ClusteringOutlier Detection	—Unverified

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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