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 2681–2690 of 4856 papers

Title	Date	Tasks	Status	Hype
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems	Jul 8, 2022	Active LearningAnomaly Detection	—Unverified	0
Deep Learning for Anomaly Detection in Log Data: A Survey	Jul 8, 2022	Anomaly DetectionDeep Learning	CodeCode Available	1
ENCODE: Encoding NetFlows for Network Anomaly Detection	Jul 8, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	0
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors	Jul 7, 2022	Anomaly DetectionAttribute	—Unverified	0
Leveraging Log Instructions in Log-based Anomaly Detection	Jul 7, 2022	Anomaly DetectionCPU	CodeCode Available	0
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition	Jul 6, 2022	Anomaly DetectionOpen Set Learning	CodeCode Available	1
BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection	Jul 5, 2022	Anomaly DetectionAutonomous Driving	CodeCode Available	0
Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection	Jul 5, 2022	Anomaly DetectionEvent Detection	CodeCode Available	1
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images	Jul 5, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available	0
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition	Jul 4, 2022	Anomaly DetectionContrastive 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