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 3021–3030 of 4856 papers

Title	Date	Tasks	Status
Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks	Dec 20, 2022	Anomaly DetectionChange Detection	—Unverified
SCNet: A Generalized Attention-based Model for Crack Fault Segmentation	Dec 2, 2021	Anomaly DetectionAnomaly Segmentation	—Unverified
Score Combining for Contrastive OOD Detection	Jan 21, 2025	Anomaly DetectionContrastive Learning	—Unverified
Scrutinizing Shipment Records To Thwart Illegal Timber Trade	Jul 31, 2022	Anomaly DetectionContrastive Learning	—Unverified
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images	May 22, 2025	Anomaly DetectionFew-Shot Learning	—Unverified
Searching for a Hidden Markov Anomaly over Multiple Processes	Jun 20, 2025	Anomaly Detection	—Unverified
Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection	Aug 15, 2023	Anomaly DetectionNovelty Detection	—Unverified
Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks	May 2, 2025	Anomaly DetectionFederated Learning	—Unverified
Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection	May 25, 2024	Anomaly DetectionFederated Learning	—Unverified
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning	Jan 30, 2019	Anomaly DetectionEnsemble 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