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 3701–3710 of 4856 papers

Title	Date	Tasks	Status	Hype
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles	Oct 28, 2020	Anomaly DetectionAutonomous Vehicles	—Unverified	0
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models	Oct 27, 2020	Anomaly DetectionChange Point Detection	CodeCode Available	1
Enhanced Cyber-Physical Security Using Attack-resistant Cyber Nodes and Event-triggered Moving Target Defence	Oct 27, 2020	Anomaly Detection	—Unverified	0
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review	Oct 27, 2020	Anomaly DetectionTime Series	—Unverified	0
Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization	Oct 24, 2020	Anomaly DetectionModel extraction	CodeCode Available	0
Low-rank on Graphs plus Temporally Smooth Sparse Decomposition for Anomaly Detection in Spatiotemporal Data	Oct 23, 2020	Anomaly DetectionTensor Decomposition	—Unverified	0
Network Anomaly Detection Using Federated Learning and Transfer Learning	Oct 22, 2020	Anomaly DetectionFederated Learning	—Unverified	0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes	Oct 22, 2020	Anomaly DetectionTime Series	CodeCode Available	0
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers	Oct 21, 2020	Anomaly DetectionKeyword Spotting	CodeCode Available	1
Anomaly Detection for Multivariate Time Series of Exotic Supernovae	Oct 21, 2020	Anomaly DetectionTime Series	—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