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 21–30 of 703 papers

Title	Date	Tasks	Status	Hype	Score
Zero-Shot Learning Through Cross-Modal Transfer	Jan 16, 2013	Outlier DetectionZero-Shot Learning	CodeCode Available	1	5
COPOD: Copula-Based Outlier Detection	Sep 20, 2020	Outlier Detection	CodeCode Available	1	5
Deep Sets	Mar 10, 2017	Anomaly DetectionOutlier Detection	CodeCode Available	1	5
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection	Feb 16, 2023	Outlier Detection	CodeCode Available	1	5
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions	Jan 2, 2022	Anomaly DetectionOutlier Detection	CodeCode Available	1	5
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement	Sep 25, 2022	Outlier Detection	CodeCode Available	1	5
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video	Aug 27, 2020	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available	1	5
Explainable outlier detection through decision tree conditioning	Jan 2, 2020	Outlier Detection	CodeCode Available	1	5
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method	Oct 24, 2022	AttributeGraph Outlier Detection	CodeCode Available	1	5
AdaLAM: Revisiting Handcrafted Outlier Detection	Jun 7, 2020	Key Point MatchingOutlier Detection	CodeCode Available	1	5

Show:10 25 50

← PrevPage 3 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