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 161–170 of 703 papers

Title	Date	Tasks	Status	Score
Efficient Generation of Hidden Outliers for Improved Outlier Detection	Feb 6, 2024	Outlier Detection	CodeCode Available	5
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection	Feb 8, 2018	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	5
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers	Aug 14, 2018	Active LearningGeneral Classification	CodeCode Available	5
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement	Dec 14, 2021	Federated LearningOutlier Detection	CodeCode Available	5
Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis	Feb 11, 2023	Image-text RetrievalKnowledge Graphs	CodeCode Available	5
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning	Apr 9, 2024	Federated LearningNetwork Pruning	CodeCode Available	5
GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories	Mar 17, 2023	Anomaly DetectionGroup Anomaly Detection	CodeCode Available	5
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces	Nov 24, 2023	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	CodeCode Available	5
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study	Feb 11, 2024	Anomaly DetectionBenchmarking	CodeCode Available	5
Depth-Based Object Tracking Using a Robust Gaussian Filter	Feb 19, 2016	Computational EfficiencyObject	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