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 481–490 of 4856 papers

Title	Date	Tasks	Status	Hype
Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity	Jan 24, 2025	Anomaly DetectionGraph Anomaly Detection	—Unverified	0
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection	Jan 24, 2025	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	0
Leveraging Digital Twin and Machine Learning Techniques for Anomaly Detection in Power Electronics Dominated Grid	Jan 23, 2025	Anomaly Detection	—Unverified	0
Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization	Jan 23, 2025	AnatomyAnomaly Detection	CodeCode Available	1
GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality	Jan 23, 2025	Anomaly DetectionTime Series	—Unverified	0
Autoencoders for Anomaly Detection are Unreliable	Jan 23, 2025	Anomaly Detection	—Unverified	0
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach	Jan 22, 2025	Anomaly DetectionDimensionality Reduction	—Unverified	0
Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection	Jan 21, 2025	Anomaly Detection	—Unverified	0
Towards Accurate Unified Anomaly Segmentation	Jan 21, 2025	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Score Combining for Contrastive OOD Detection	Jan 21, 2025	Anomaly DetectionContrastive Learning	—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