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 71–80 of 703 papers

Title	Date	Tasks	Status	Hype
PyOD: A Python Toolbox for Scalable Outlier Detection	Jan 6, 2019	Anomaly DetectionOutlier Detection	CodeCode Available	1
LSTM Fully Convolutional Networks for Time Series Classification	Sep 8, 2017	General ClassificationOutlier Detection	CodeCode Available	1
Deep Sets	Mar 10, 2017	Anomaly DetectionOutlier Detection	CodeCode Available	1
Zero-Shot Learning Through Cross-Modal Transfer	Jan 16, 2013	Outlier DetectionZero-Shot Learning	CodeCode Available	1
Robust Spatiotemporal Epidemic Modeling with Integrated Adaptive Outlier Detection	Jul 12, 2025	Outlier Detectionparameter estimation	CodeCode Available	0
Universal Embeddings of Tabular Data	Jul 8, 2025	Entity EmbeddingsOutlier Detection	—Unverified	0
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles	Jun 15, 2025	Anomaly DetectionAnomaly Forecasting	—Unverified	0
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers	May 30, 2025	Outlier Detection	CodeCode Available	0
LayerIF: Estimating Layer Quality for Large Language Models using Influence Functions	May 27, 2025	Outlier Detection	—Unverified	0
Learning novel representations of variable sources from multi-modal Gaia data via autoencoders	May 22, 2025	Data CompressionOutlier Detection	—Unverified	0

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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