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 1401–1410 of 4856 papers

Title	Date	Tasks	Status
Detecting Anomalous User Behavior in Remote Patient Monitoring	Jun 22, 2021	Anomaly Detection	—Unverified
AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings	Nov 17, 2024	Anomaly DetectionArrhythmia Detection	—Unverified
An Efficient Outlier Detection Algorithm for Data Streaming	Jan 2, 2025	Anomaly DetectionFraud Detection	—Unverified
ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data	Aug 28, 2024	Anomaly DetectionCode Generation	—Unverified
An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS	Dec 24, 2015	Anomaly Detection	—Unverified
An Efficient One-Class SVM for Anomaly Detection in the Internet of Things	Apr 22, 2021	Anomaly DetectionNovelty Detection	—Unverified
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection	May 22, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis	Jan 7, 2022	Anomaly Detection	—Unverified
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier	Apr 2, 2019	Anomaly DetectionDimensionality Reduction	—Unverified
An Efficient Approach for Anomaly Detection in Traffic Videos	Apr 20, 2021	Anomaly DetectionChange 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	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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