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 321–330 of 4856 papers

Title	Date	Tasks	Status	Hype
Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusion	Mar 14, 2025	Anomaly Detection	—Unverified	0
Federated Koopman-Reservoir Learning for Large-Scale Multivariate Time-Series Anomaly Detection	Mar 14, 2025	Anomaly DetectionFederated Learning	—Unverified	0
Hoi2Anomaly: An Explainable Anomaly Detection Approach Guided by Human-Object Interaction	Mar 13, 2025	Anomaly DetectionHuman-Object Interaction Detection	—Unverified	0
Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection	Mar 13, 2025	Anomaly Detectionzero-shot anomaly detection	CodeCode Available	2
G^2SF-MIAD: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detection	Mar 13, 2025	Anomaly Detection	—Unverified	0
Time-EAPCR: A Deep Learning-Based Novel Approach for Anomaly Detection Applied to the Environmental Field	Mar 12, 2025	Anomaly Detection	—Unverified	0
STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications	Mar 11, 2025	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows	Mar 11, 2025	Anomaly DetectionArticles	—Unverified	0
A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph	Mar 10, 2025	Anomaly DetectionDecoder	—Unverified	0
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos	Mar 10, 2025	Anomaly DetectionPrivacy Preserving	—Unverified	0

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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