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 3221–3230 of 4856 papers

Title	Date	Tasks	Status	Hype
Weakly-supervised Joint Anomaly Detection and Classification	Aug 20, 2021	Anomaly DetectionClassification	—Unverified	0
CloudShield: Real-time Anomaly Detection in the Cloud	Aug 20, 2021	Anomaly DetectionCloud Computing	CodeCode Available	0
Abnormal Road Surface Detection Using Wheel Sensor Data	Aug 20, 2021	Anomaly DetectionBinary Classification	—Unverified	0
Federated Variational Learning for Anomaly Detection in Multivariate Time Series	Aug 18, 2021	Anomaly DetectionRepresentation Learning	—Unverified	0
Graph Contrastive Learning for Anomaly Detection	Aug 17, 2021	Anomaly DetectionBinary Classification	CodeCode Available	1
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection	Aug 17, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction	Aug 16, 2021	Anomaly DetectionOptical Flow Estimation	CodeCode Available	1
Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection	Aug 15, 2021	Anomaly DetectionTime Series	—Unverified	0
Random Subspace Mixture Models for Interpretable Anomaly Detection	Aug 13, 2021	Anomaly DetectionAttribute	—Unverified	0
Unconditional Scene Graph Generation	Aug 12, 2021	Anomaly DetectionGraph Generation	—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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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