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
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
Computationally Assisted Quality Control for Public Health Data Streams	Jun 29, 2023	Decision MakingOutlier Detection	CodeCode Available	1
Zero-Shot Learning Through Cross-Modal Transfer	Jan 16, 2013	Outlier DetectionZero-Shot Learning	CodeCode Available	1
Deep Clustering based Fair Outlier Detection	Jun 9, 2021	AttributeClustering	CodeCode Available	1
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection	Feb 16, 2023	Outlier Detection	CodeCode Available	1
DEUP: Direct Epistemic Uncertainty Prediction	Feb 16, 2021	Active Learningimage-classification	CodeCode Available	1
Energy-based Detection of Adverse Weather Effects in LiDAR Data	May 25, 2023	Autonomous VehiclesOutlier Detection	CodeCode Available	1
Explainable Deep One-Class Classification	Jul 3, 2020	Anomaly DetectionClassification	CodeCode Available	1
Explaining Anomalies Detected by Autoencoders Using SHAP	Mar 6, 2019	Anomaly DetectionOutlier Detection	CodeCode Available	1
AdaLAM: Revisiting Handcrafted Outlier Detection	Jun 7, 2020	Key Point MatchingOutlier Detection	CodeCode Available	1

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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