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 2431–2440 of 4856 papers

Title	Date	Tasks	Status
An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS	Dec 24, 2015	Anomaly Detection	—Unverified
ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data	Aug 28, 2024	Anomaly DetectionCode Generation	—Unverified
AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings	Nov 17, 2024	Anomaly DetectionArrhythmia Detection	—Unverified
Any-Shot Sequential Anomaly Detection in Surveillance Videos	Apr 5, 2020	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
Anytime Hierarchical Clustering	Apr 13, 2014	Anomaly DetectionClustering	—Unverified
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection	Feb 18, 2019	Anomaly DetectionChange Point Detection	—Unverified
APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks	Feb 13, 2024	Anomaly DetectionDeep Learning	—Unverified
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection	Nov 4, 2021	Anomaly DetectionFederated Learning	—Unverified
A Physics-Based Context-Aware Approach for Anomaly Detection in Teleoperated Driving Operations Under False Data Injection Attacks	Oct 17, 2024	Anomaly DetectionSteering Control	—Unverified
A Poisson Process AutoDecoder for X-ray Sources	Feb 3, 2025	Anomaly DetectionDecoder	—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