Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3681–3690 of 4856 papers

Title	Date	Tasks	Status
Machine Learning-Based Cloud Computing Compliance Process Automation	Feb 22, 2025	Anomaly DetectionCloud Computing	—Unverified
Machine Learning-based Layer-wise Detection of Overheating Anomaly in LPBF using Photodiode Data	Mar 20, 2024	Anomaly Detection	—Unverified
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things	Nov 13, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning-Based Security Policy Analysis	Dec 30, 2024	Anomaly Detection	—Unverified
Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors	Sep 26, 2024	Anomaly DetectionTime Series	—Unverified
Machine learning-driven Anomaly Detection and Forecasting for Euclid Space Telescope Operations	Nov 8, 2024	Anomaly Detection	—Unverified
Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments	Oct 23, 2018	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning for Anomaly Detection in Particle Physics	Dec 20, 2023	Anomaly Detection	—Unverified
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method	Jul 5, 2024	Anomaly DetectionOutlier Detection	—Unverified
Machine Learning for Drug Overdose Surveillance	Oct 6, 2017	Anomaly DetectionBIG-bench Machine Learning	—Unverified

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified