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 3691–3700 of 4856 papers

Title	Date	Tasks	Status
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations	Feb 24, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning for Real-Time Anomaly Detection in Optical Networks	Jun 19, 2023	Anomaly DetectionDecoder	—Unverified
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey	Jan 25, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning in NextG Networks via Generative Adversarial Networks	Mar 9, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers	Nov 20, 2022	Anomaly DetectionTime Series Analysis	—Unverified
Machine Learning with DBOS	Aug 10, 2022	Anomaly Detectionimage-classification	—Unverified
Machine Learning with Memristors via Thermodynamic RAM	Aug 14, 2016	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning with Probabilistic Law Discovery: A Concise Introduction	Dec 22, 2022	Anomaly DetectionTime Series	—Unverified
MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network	May 22, 2025	Anomaly DetectionClustering	—Unverified
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction	Jul 24, 2020	Anomaly DetectionGenerative Adversarial Network	—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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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