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 1951–1960 of 4856 papers

Title	Date	Tasks	Status
An Adaptive Event-based Data Converter for Always-on Biomedical Applications at the Edge	Nov 23, 2022	Anomaly Detection	—Unverified
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks	May 1, 2022	Anomaly Detectionfeature selection	—Unverified
Federated Variational Learning for Anomaly Detection in Multivariate Time Series	Aug 18, 2021	Anomaly DetectionRepresentation Learning	—Unverified
GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract	Oct 7, 2014	Anomaly DetectionGroup Anomaly Detection	—Unverified
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle	Sep 28, 2021	Anomaly DetectionTime Series	—Unverified
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy	Oct 6, 2023	Anomaly DetectionSelf-Supervised Anomaly Detection	—Unverified
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection	Apr 19, 2023	Activity RecognitionAnomaly Detection	—Unverified
Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison	Jun 19, 2021	Anomaly DetectionAnomaly Localization	—Unverified
FEMa-FS: Finite Element Machines for Feature Selection	Dec 5, 2022	Anomaly Detectionfeature selection	—Unverified
Crowd Scene Analysis using Deep Learning Techniques	May 13, 2025	Anomaly DetectionBinary Classification	—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