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 2791–2800 of 4856 papers

Title	Date	Tasks	Status	Hype
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook	May 10, 2022	Anomaly DetectionSelf-Supervised Anomaly Detection	—Unverified	0
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs	May 9, 2022	AnatomyAnomaly Detection	CodeCode Available	1
Deep Federated Anomaly Detection for Multivariate Time Series Data	May 9, 2022	Anomaly DetectionConstrained Clustering	—Unverified	0
Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature	May 8, 2022	Anomaly DetectionDiversity	—Unverified	0
Anomaly Detection in Intra-Vehicle Networks	May 7, 2022	Anomaly DetectionIntrusion Detection	—Unverified	0
LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding	May 5, 2022	Anomaly DetectionCloud Computing	—Unverified	0
Explainable Anomaly Detection for Industrial Control System Cybersecurity	May 4, 2022	Anomaly DetectionExplainable artificial intelligence	CodeCode Available	0
Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training	May 4, 2022	Anomaly DetectionPrediction	—Unverified	0
MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series	May 4, 2022	Anomaly DetectionSelf-Supervised Learning	CodeCode Available	1
A Contrario multi-scale anomaly detection method for industrial quality inspection	May 3, 2022	Anomaly Detection	—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