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 51–60 of 703 papers

Title	Date	Tasks	Status	Hype
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control	Oct 3, 2021	BIG-bench Machine LearningInstance Segmentation	CodeCode Available	1
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
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks	Dec 10, 2021	Anomaly DetectionGraph Neural Network	CodeCode Available	1
MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning	Oct 15, 2023	Graph Representation LearningOutlier Detection	CodeCode Available	1
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures	Jun 27, 2023	ClusteringOutlier Detection	CodeCode Available	1
On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insights	Dec 23, 2020	Binary ClassificationGeneral Classification	CodeCode Available	1
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization	Dec 1, 2021	Novelty DetectionOutlier Detection	CodeCode Available	1
Open-Set Likelihood Maximization for Few-Shot Learning	Jan 20, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language	Nov 8, 2022	Outlier Detection	CodeCode Available	1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network	Apr 19, 2021	Anomaly DetectionOutlier 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