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 3841–3850 of 4856 papers

Title	Date	Tasks	Status
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions	Jan 2, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic	Dec 11, 2013	Anomaly Detection	—Unverified
Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point-wise Anomaly Detection	Jan 17, 2020	Anomaly DetectionDimensionality Reduction	—Unverified
Network Anomaly Detection: A Survey and Comparative Analysis of Stochastic and Deterministic Methods	Sep 19, 2013	Anomaly DetectionClustering	—Unverified
Network Anomaly Detection based on Tensor Decomposition	Apr 20, 2020	Anomaly DetectionTensor Decomposition	—Unverified
Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD	Mar 4, 2025	Anomaly DetectionIntrusion Detection	—Unverified
Network Anomaly Detection Using Federated Learning and Transfer Learning	Oct 22, 2020	Anomaly DetectionFederated Learning	—Unverified
Network Anomaly Detection Using Federated Learning	Mar 13, 2023	Anomaly DetectionFederated Learning	—Unverified
Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring	Jul 5, 2019	Anomaly Detection	—Unverified
Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature	May 8, 2022	Anomaly DetectionDiversity	—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