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 1741–1750 of 4856 papers

Title	Date	Tasks	Status
Anomaly Detection from a Tensor Train Perspective	Sep 23, 2024	Anomaly DetectionData Compression	—Unverified
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection	Aug 5, 2020	Anomaly DetectionBayesian Optimization	—Unverified
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection	Oct 28, 2014	Anomaly DetectionBIG-bench Machine Learning	—Unverified
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data	Aug 25, 2023	Anomaly DetectionTime Series	—Unverified
Bayesian Learning of Clique Tree Structure	Aug 23, 2017	Anomaly DetectionClustering	—Unverified
Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization	Jul 19, 2018	Anomaly Detection	—Unverified
Bayesian Hypernetworks	Oct 13, 2017	Active LearningAnomaly Detection	—Unverified
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content	Mar 21, 2025	Anomaly DetectionData Visualization	—Unverified
Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding	Mar 9, 2022	Anomaly DetectionFault Detection	—Unverified
A general-purpose method for applying Explainable AI for Anomaly Detection	Jul 23, 2022	Anomaly DetectionExplainable Artificial Intelligence (XAI)	—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