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 441–450 of 4856 papers

Title	Date	Tasks	Status	Hype
GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection	Jun 21, 2023	Anomaly DetectionBenchmarking	CodeCode Available	1
BMAD: Benchmarks for Medical Anomaly Detection	Jun 20, 2023	Anomaly DetectionMedical Diagnosis	CodeCode Available	1
Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement	Jun 19, 2023	Anomaly DetectionFraud Detection	CodeCode Available	1
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation	Jun 18, 2023	Anomaly DetectionData Augmentation	CodeCode Available	1
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction	Jun 5, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
UADB: Unsupervised Anomaly Detection Booster	Jun 3, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn	Jun 3, 2023	Anomaly DetectionDeep Learning	CodeCode Available	1
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction	Jun 2, 2023	Anomaly DetectionFraud Detection	CodeCode Available	1
Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model	May 31, 2023	AnatomyAnomaly Detection	CodeCode Available	1
Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models	May 31, 2023	Anomaly DetectionImage Reconstruction	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