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 151–160 of 4856 papers

Title	Date	Tasks	Status	Hype
GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models	May 12, 2025	Anomaly DetectionDiagnostic	—Unverified	0
Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study	May 12, 2025	Anomaly Detection	—Unverified	0
Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression	May 11, 2025	Anomaly DetectionData Compression	—Unverified	0
ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection	May 10, 2025	Anomaly Detectioncontinual anomaly detection	CodeCode Available	2
AI-Powered Anomaly Detection with Blockchain for Real-Time Security and Reliability in Autonomous Vehicles	May 10, 2025	Anomaly DetectionAutonomous Vehicles	—Unverified	0
Engineering Risk-Aware, Security-by-Design Frameworks for Assurance of Large-Scale Autonomous AI Models	May 9, 2025	Anomaly Detection	—Unverified	0
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection	May 9, 2025	3D Anomaly Detection3D Anomaly Detection and Segmentation	CodeCode Available	0
Research on Anomaly Detection Methods Based on Diffusion Models	May 8, 2025	Anomaly DetectionFault Diagnosis	—Unverified	0
Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment	May 7, 2025	Anomaly DetectionFraud Detection	—Unverified	0
Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts	May 7, 2025	Anomaly DetectionGrammatical Error Detection	—Unverified	0

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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	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