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 611–620 of 703 papers

Title	Date	Tasks	Status
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models	Jan 11, 2023	MemorizationOutlier Detection	CodeCode Available
Outlier Detection in Large Radiological Datasets using UMAP	Jul 31, 2024	Outlier Detection	CodeCode Available
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty	Jun 28, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
Weighted Scaling Approach for Metabolomics Data Analysis	Aug 1, 2022	Outlier Detection	CodeCode Available
Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis	Feb 11, 2023	Image-text RetrievalKnowledge Graphs	CodeCode Available
SDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale Datasets	Jun 13, 2020	ClusteringOutlier Detection	CodeCode Available
Anomaly Detection With Partitioning Overfitting Autoencoder Ensembles	Sep 6, 2020	Anomaly DetectionOutlier Detection	CodeCode Available
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach	Sep 14, 2018	Anomaly DetectionCyber Attack Detection	CodeCode Available
PyODDS: An End-to-End Outlier Detection System	Oct 7, 2019	BIG-bench Machine LearningOutlier Detection	CodeCode Available
What Do Compressed Deep Neural Networks Forget?	Nov 13, 2019	FairnessInterpretability Techniques for Deep Learning	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