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 2541–2550 of 4856 papers

Title	Date	Tasks	Status
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions	Oct 28, 2024	Anomaly DetectionFairness	—Unverified
ATAC-Net: Zoomed view works better for Anomaly Detection	Jun 20, 2024	Anomaly Detection	—Unverified
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference	Jan 23, 2023	Anomaly DetectionImage Generation	—Unverified
A task of anomaly detection for a smart satellite Internet of things system	Mar 21, 2024	Anomaly DetectionGenerative Adversarial Network	—Unverified
A Taxonomy of Anomalies in Log Data	Nov 26, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels	Sep 14, 2022	Anomaly Detection	—Unverified
A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments	Oct 11, 2024	Anomaly DetectionManagement	—Unverified
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure	Jan 24, 2018	Anomaly DetectionClustering	—Unverified
A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph	Mar 10, 2025	Anomaly DetectionDecoder	—Unverified
Atom dimension adaptation for infinite set dictionary learning	Sep 10, 2024	Anomaly DetectionDictionary 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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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