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 591–600 of 703 papers

Title	Date	Tasks	Status
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning	Feb 6, 2025	ImputationOutlier Detection	CodeCode Available
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder	May 5, 2021	Autonomous VehiclesNovelty Detection	CodeCode Available
Fluctuation-based Outlier Detection	Apr 21, 2022	Outlier Detection	CodeCode Available
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers	May 30, 2025	Outlier Detection	CodeCode Available
Further Analysis of Outlier Detection with Deep Generative Models	Oct 25, 2020	Outlier Detection	CodeCode Available
Distribution and volume based scoring for Isolation Forests	Sep 20, 2023	Outlier Detection	CodeCode Available
GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories	Mar 17, 2023	Anomaly DetectionGroup Anomaly Detection	CodeCode Available
Nowcasting Networks	Nov 27, 2020	Outlier DetectionTime Series	CodeCode Available
Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models	Dec 29, 2024	Bayesian InferenceData Augmentation	CodeCode Available
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces	Nov 24, 2023	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	CodeCode Available

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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