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 2031–2040 of 4856 papers

Title	Date	Tasks	Status
Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection	Dec 29, 2024	Anomaly DetectionGraph Attention	—Unverified
Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security	Nov 10, 2021	Anomaly DetectionState Estimation	—Unverified
Cross-Entropy Games for Language Models: From Implicit Knowledge to General Capability Measures	Jun 7, 2025	Anomaly Detectioncounterfactual	—Unverified
A Deep Look Into -- Automated Lung X-Ray Abnormality Detection System	Apr 6, 2024	Anomaly Detection	—Unverified
Bayesian Time Series Forecasting with Change Point and Anomaly Detection	Jan 1, 2018	Anomaly DetectionChange Point Detection	—Unverified
FadMan: Federated Anomaly Detection across Multiple Attributed Networks	May 27, 2022	Anomaly DetectionData Integration	—Unverified
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization	Oct 9, 2021	Anomaly DetectionContrastive Learning	—Unverified
Fair Anomaly Detection For Imbalanced Groups	Sep 17, 2024	Anomaly DetectionContrastive Learning	—Unverified
Cross-Domain Video Anomaly Detection without Target Domain Adaptation	Dec 14, 2022	Anomaly DetectionDomain Adaptation	—Unverified
Cross-Domain Learning for Video Anomaly Detection with Limited Supervision	Aug 9, 2024	Anomaly DetectionVideo 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