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 3671–3680 of 4856 papers

Title	Date	Tasks	Status
M3A: Model, MetaModel, and Anomaly Detection in Web Searches	Jun 20, 2016	Anomaly Detection	—Unverified
M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising	Jun 4, 2024	Anomaly DetectionDenoising	—Unverified
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection	Nov 26, 2023	Anomaly DetectionTime Series	—Unverified
Machine Learning and Data Science approach towards trend and predictors analysis of CDC Mortality Data for the USA	Sep 11, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning Applications in Misuse and Anomaly Detection	Sep 10, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data	Feb 10, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning based Anomaly Detection for 5G Networks	Mar 7, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning based Anomaly Detection for Smart Shirt: A Systematic Review	Mar 7, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning-based Anomaly Detection in Optical Fiber Monitoring	Mar 19, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach	May 29, 2025	Anomaly DetectionHybrid Machine 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	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