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 91–100 of 703 papers

Title	Date	Tasks	Status	Hype
Meta-learning for Positive-unlabeled Classification	Jun 6, 2024	ClassificationDensity Ratio Estimation	—Unverified	0
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS	Jun 4, 2024	Neural Architecture SearchOutlier Detection	—Unverified	0
Comparative Study of Neighbor-based Methods for Local Outlier Detection	May 29, 2024	Outlier Detection	—Unverified	0
EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy	May 21, 2024	Outlier Detection	CodeCode Available	0
Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective	May 17, 2024	Backdoor AttackMemorization	CodeCode Available	1
A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning	Apr 27, 2024	image-classificationImage Classification	CodeCode Available	0
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data	Apr 23, 2024	Anomaly DetectionFederated Learning	—Unverified	0
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Data	Apr 20, 2024	Active LearningGenerative Adversarial Network	CodeCode Available	0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning	Apr 9, 2024	Federated LearningNetwork Pruning	CodeCode Available	0
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability	Apr 9, 2024	Anomaly DetectionOutlier 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