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 1881–1890 of 4856 papers

Title	Date	Tasks	Status	Hype
A Deep-Learning Method Using Auto-encoder and Generative Adversarial Network for Anomaly Detection on Ancient Stone Stele Surfaces	Aug 8, 2023	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
Implementing Immune Repertoire Models Using Weighted Finite State Machines	Aug 7, 2023	Anomaly Detection	—Unverified	0
Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection	Aug 6, 2023	Anomaly DetectionSelf-Supervised Learning	CodeCode Available	1
Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques	Aug 6, 2023	Anomaly DetectionTime Series	—Unverified	0
Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph	Aug 5, 2023	Anomaly DetectionFraud Detection	CodeCode Available	0
Anomaly Detection in Global Financial Markets with Graph Neural Networks and Nonextensive Entropy	Aug 5, 2023	Anomaly Detection	—Unverified	0
Synthetic outlier generation for anomaly detection in autonomous driving	Aug 4, 2023	Anomaly DetectionAutonomous Driving	—Unverified	0
Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model	Aug 3, 2023	Anomaly Detection	CodeCode Available	0
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images	Aug 3, 2023	AnatomyAnomaly Detection	CodeCode Available	1
UGainS: Uncertainty Guided Anomaly Instance Segmentation	Aug 3, 2023	Anomaly DetectionAnomaly Instance Segmentation	CodeCode Available	1

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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