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 2941–2950 of 4856 papers

Title	Date	Tasks	Status
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning	Sep 23, 2024	Anomaly DetectionClustering	—Unverified
Residual ANODE	Dec 18, 2023	Anomaly DetectionInductive Bias	—Unverified
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End	Oct 5, 2023	Anomaly DetectionCPU	—Unverified
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source	Sep 5, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Resonant Anomaly Detection with Multiple Reference Datasets	Dec 20, 2022	Anomaly Detection	—Unverified
Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals	Nov 2, 2023	Anomaly Detection	—Unverified
Restricted Generative Projection for One-Class Classification and Anomaly Detection	Jul 9, 2023	Anomaly DetectionInformativeness	—Unverified
ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features	Dec 27, 2023	Anomaly DetectionImage Reconstruction	—Unverified
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective	May 23, 2025	Anomaly DetectionContrastive Learning	—Unverified
Rethinking Metrics and Benchmarks of Video Anomaly Detection	May 25, 2025	Anomaly DetectionVideo Anomaly Detection	—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