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 561–570 of 703 papers

Title	Date	Tasks	Status
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes	Dec 27, 2023	ManagementOutlier Detection	CodeCode Available
Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detection	Jun 9, 2021	Drift DetectionNovelty Detection	CodeCode Available
EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy	May 21, 2024	Outlier Detection	CodeCode Available
Estimating Density Models with Truncation Boundaries using Score Matching	Oct 9, 2019	Density EstimationOutlier Detection	CodeCode Available
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging	Jul 15, 2022	Federated LearningOutlier Detection	CodeCode Available
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning	Sep 27, 2018	DiversityDomain Adaptation	CodeCode Available
Word Embeddings via Tensor Factorization	Apr 10, 2017	Outlier DetectionWord Embeddings	CodeCode Available
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network	Dec 1, 2019	Outlier DetectionRepresentation Learning	CodeCode Available
Edgewise outliers of network indexed signals	Jul 20, 2023	Outlier Detection	CodeCode Available
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?	Nov 9, 2023	Meta-LearningModel Selection	CodeCode Available

Show:10 25 50

← PrevPage 57 of 71Next →

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