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 2821–2830 of 4856 papers

Title	Date	Tasks	Status
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch	Nov 30, 2022	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
Unsupervised Visual Defect Detection with Score-Based Generative Model	Nov 29, 2022	Anomaly DetectionDefect Detection	—Unverified
G-CMP: Graph-enhanced Contextual Matrix Profile for unsupervised anomaly detection in sensor-based remote health monitoring	Nov 29, 2022	Anomaly Detectiongraph construction	—Unverified
Novelty Detection for Election Fraud: A Case Study with Agent-Based Simulation Data	Nov 29, 2022	Anomaly DetectionNovelty Detection	—Unverified
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness	Nov 28, 2022	Anomaly DetectionAttribute	—Unverified
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI	Nov 28, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation	Nov 28, 2022	Anomaly DetectionKnowledge Distillation	CodeCode Available
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout	Nov 27, 2022	Anomaly DetectionTime Series	—Unverified
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection	Nov 25, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset	Nov 25, 2022	Anomaly DetectionGenerative Adversarial Network	—Unverified

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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