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 2571–2580 of 4856 papers

Title	Date	Tasks	Status
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection	May 25, 2023	Anomaly DetectionModel Optimization	—Unverified
RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series	May 25, 2023	Anomaly DetectionTime Series	—Unverified
Anomaly Detection in Satellite Videos using Diffusion Models	May 25, 2023	Anomaly DetectionManagement	—Unverified
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision	May 25, 2023	Anomaly DetectionContrastive Learning	—Unverified
Real time dense anomaly detection by learning on synthetic negative data	May 24, 2023	Anomaly Detection	—Unverified
Multiresolution Feature Guidance Based Transformer for Anomaly Detection	May 24, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos	May 24, 2023	Anomaly Detection	CodeCode Available
A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation	May 23, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection	May 22, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection	May 21, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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