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 2941–2950 of 4856 papers

Title	Date	Tasks	Status	Hype
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series	Feb 16, 2022	Anomaly DetectionDensity Estimation	CodeCode Available	3
Latent Outlier Exposure for Anomaly Detection with Contaminated Data	Feb 16, 2022	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1
Federated-Learning-Based Anomaly Detection for IoT Security Attacks	Feb 15, 2022	Anomaly DetectionFederated Learning	—Unverified	0
Trustworthy Anomaly Detection: A Survey	Feb 15, 2022	Anomaly DetectionFairness	—Unverified	0
Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset	Feb 15, 2022	Anomaly DetectionAutonomous Vehicles	—Unverified	0
Transformers in Time Series: A Survey	Feb 15, 2022	Anomaly DetectionSurvey	CodeCode Available	4
Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes	Feb 15, 2022	Anomaly DetectionBinary Classification	—Unverified	0
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection	Feb 15, 2022	Anomaly DetectionMissing Values	CodeCode Available	1
A Survey of Visual Sensory Anomaly Detection	Feb 14, 2022	Anomaly DetectionSurvey	CodeCode Available	1
DeCorus: Hierarchical Multivariate Anomaly Detection at Cloud-Scale	Feb 14, 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	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