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 1861–1870 of 4856 papers

Title	Date	Tasks	Status
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing	Aug 9, 2024	Anomaly DetectionDefect Detection	—Unverified
Energy-Based Anomaly Detection and Localization	May 7, 2021	Anomaly Detection	—Unverified
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach	Oct 28, 2023	Anomaly Detection	—Unverified
Energy-based Models for Video Anomaly Detection	Aug 17, 2017	Anomaly DetectionFeature Engineering	—Unverified
Energy-Efficient Classification for Anomaly Detection: The Wireless Channel as a Helper	Dec 15, 2015	Anomaly DetectionFault Diagnosis	—Unverified
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants	Feb 21, 2022	Anomaly DetectionModel Selection	—Unverified
An AI-Based Public Health Data Monitoring System	Jun 4, 2025	Anomaly DetectionDecision Making	—Unverified
Enforcing Cybersecurity Constraints for LLM-driven Robot Agents for Online Transactions	Mar 17, 2025	Anomaly Detection	—Unverified
EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models	May 1, 2019	Anomaly DetectionNovelty Detection	—Unverified
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation	Jul 20, 2023	Anomaly 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