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 281–290 of 4856 papers

Title	Date	Tasks	Status	Hype
The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection	Mar 27, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection	Mar 26, 2025	Anomaly DetectionMamba	—Unverified	0
Channel impulse response peak clustering using neural networks	Mar 26, 2025	Anomaly DetectionClustering	—Unverified	0
Refining Time Series Anomaly Detectors using Large Language Models	Mar 26, 2025	Anomaly DetectionTime Series	—Unverified	0
LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions	Mar 26, 2025	Anomaly DetectionLanguage Modeling	—Unverified	0
β-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation	Mar 26, 2025	Anomaly DetectionManagement	CodeCode Available	0
Post-Hoc Calibrated Anomaly Detection	Mar 25, 2025	Anomaly DetectionBinary Classification	—Unverified	0
Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks	Mar 25, 2025	Anomaly DetectionComputational Efficiency	—Unverified	0
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection	Mar 25, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	2
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics	Mar 24, 2025	Anomaly DetectionComputational Efficiency	—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