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 2631–2640 of 4856 papers

Title	Date	Tasks	Status
Bayesian Hypernetworks	Oct 13, 2017	Active LearningAnomaly Detection	—Unverified
Bayesian Learning of Clique Tree Structure	Aug 23, 2017	Anomaly DetectionClustering	—Unverified
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection	Aug 5, 2020	Anomaly DetectionBayesian Optimization	—Unverified
Bayesian Time Series Forecasting with Change Point and Anomaly Detection	Jan 1, 2018	Anomaly DetectionChange Point Detection	—Unverified
Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning	Jun 24, 2025	Anomaly DetectionContrastive Learning	—Unverified
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics	Sep 21, 2022	Anomaly DetectionBenchmarking	—Unverified
Benchmarking Unsupervised Anomaly Detection and Localization	May 30, 2022	Anomaly DetectionBenchmarking	—Unverified
Beta quantile regression for robust estimation of uncertainty in the presence of outliers	Sep 14, 2023	Anomaly DetectionImage Reconstruction	—Unverified
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization	Feb 22, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Beyond Forecasting: Compositional Time Series Reasoning for End-to-End Task Execution	Oct 5, 2024	Anomaly DetectionDecision Making	—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