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 981–990 of 4856 papers

Title	Date	Tasks	Status
Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs	Jun 5, 2025	Anomaly Detection	—Unverified
Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems	Jun 5, 2025	Anomaly DetectionFederated Learning	—Unverified
An AI-Based Public Health Data Monitoring System	Jun 4, 2025	Anomaly DetectionDecision Making	—Unverified
System Calls for Malware Detection and Classification: Methodologies and Applications	Jun 2, 2025	Anomaly DetectionMalware Analysis	—Unverified
Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC	May 30, 2025	Anomaly DetectionQuantum Machine Learning	CodeCode Available
Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation	May 30, 2025	3D Anomaly DetectionAnomaly Detection	CodeCode Available
Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm	May 30, 2025	Anomaly Detection	—Unverified
SentinelAgent: Graph-based Anomaly Detection in Multi-Agent Systems	May 30, 2025	Anomaly DetectionLarge Language Model	—Unverified
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series	May 30, 2025	Anomaly DetectionTime Series	—Unverified
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices	May 30, 2025	Anomaly Detection	CodeCode Available

Show:10 25 50

← PrevPage 99 of 486Next →

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	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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