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 1–10 of 703 papers

Title	Date	Tasks	Status	Hype
ADBench: Anomaly Detection Benchmark	Jun 19, 2022	Anomaly DetectionOutlier Detection	CodeCode Available	3
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning	Jun 7, 2022	AutoMLBIG-bench Machine Learning	CodeCode Available	3
Interactive Continual Learning: Fast and Slow Thinking	Mar 5, 2024	Continual LearningOutlier Detection	CodeCode Available	2
TODS: An Automated Time Series Outlier Detection System	Sep 18, 2020	Outlier DetectionTime Series	CodeCode Available	2
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection	Jul 1, 2016	Anomaly DetectionDecoder	CodeCode Available	2
Towards Total Recall in Industrial Anomaly Detection	Jun 15, 2021	3D Anomaly DetectionAnomaly Classification	CodeCode Available	2
Autoencoding Under Normalization Constraints	May 12, 2021	Outlier Detection	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
Automating Outlier Detection via Meta-Learning	Sep 22, 2020	Anomaly DetectionAutoML	CodeCode Available	1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching	Oct 17, 2019	DenoisingImage Inpainting	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