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 141–150 of 703 papers

Title	Date	Tasks	Status	Score
Efficient Curation of Invertebrate Image Datasets Using Feature Embeddings and Automatic Size Comparison	Dec 20, 2024	Outlier Detection	CodeCode Available	5
Anomaly Detection with Selective Dictionary Learning	Jul 17, 2023	Anomaly DetectionDictionary Learning	CodeCode Available	5
Anomaly Detection With Partitioning Overfitting Autoencoder Ensembles	Sep 6, 2020	Anomaly DetectionOutlier Detection	CodeCode Available	5
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions	Feb 27, 2018	ClusteringOutlier Detection	CodeCode Available	5
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers	May 30, 2025	Outlier Detection	CodeCode Available	5
D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments	Oct 15, 2022	ClusteringOutlier Detection	CodeCode Available	5
Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis	Feb 11, 2023	Image-text RetrievalKnowledge Graphs	CodeCode Available	5
Anomaly Detection via oversampling Principal Component Analysis	Jul 1, 2013	Anomaly DetectionOutlier Detection	CodeCode Available	5
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning	Feb 6, 2025	ImputationOutlier Detection	CodeCode Available	5
Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis	Jan 10, 2024	Outlier Detection	CodeCode Available	5

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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