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 411–420 of 4856 papers

Title	Date	Tasks	Status	Hype
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition	Aug 15, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available	1
Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies	Aug 15, 2023	Anomaly DetectionLog Parsing	CodeCode Available	1
Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval	Aug 13, 2023	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	1
Out-of-Distribution Detection for Monocular Depth Estimation	Aug 11, 2023	Anomaly DetectionDecoder	CodeCode Available	1
Multi-Class Deep SVDD: Anomaly Detection Approach in Astronomy with Distinct Inlier Categories	Aug 9, 2023	Anomaly DetectionAstronomy	CodeCode Available	1
Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection	Aug 6, 2023	Anomaly DetectionSelf-Supervised Learning	CodeCode Available	1
Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection	Aug 3, 2023	Anomaly DetectionDiagnostic	CodeCode Available	1
UGainS: Uncertainty Guided Anomaly Instance Segmentation	Aug 3, 2023	Anomaly DetectionAnomaly Instance Segmentation	CodeCode Available	1
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images	Aug 3, 2023	AnatomyAnomaly Detection	CodeCode Available	1
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach	Aug 2, 2023	Anomaly DetectionDeep Learning	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