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 2271–2280 of 4856 papers

Title	Date	Tasks	Status
A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets	May 14, 2023	Anomaly DetectionComputational Efficiency	—Unverified
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI	Mar 24, 2021	Anomaly DetectionLesion Detection	—Unverified
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments	Dec 21, 2023	Anomaly DetectionFraud Detection	—Unverified
Community-Level Anomaly Detection for Anti-Money Laundering	Oct 24, 2019	Anomaly DetectionDictionary Learning	—Unverified
Community-based anomaly detection using spectral graph filtering	Jan 24, 2022	Anomaly DetectionOutlier Detection	—Unverified
Combining Switching Mechanism with Re-Initialization and Anomaly Detection for Resiliency of Cyber-Physical Systems	Sep 21, 2024	Anomaly Detection	—Unverified
Anomaly Detection of UAV State Data Based on Single-class Triangular Global Alignment Kernel Extreme Learning Machine	Feb 18, 2023	Anomaly Detection	—Unverified
A Machine-Synesthetic Approach To DDoS Network Attack Detection	Jan 13, 2019	Anomaly DetectionGeneral Classification	—Unverified
Heterogeneous Relational Kernel Learning	Aug 24, 2019	Anomaly DetectionClustering	—Unverified
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection	Nov 1, 2022	Anomaly DetectionGraph structure learning	—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