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 2441–2450 of 4856 papers

Title	Date	Tasks	Status
Sparse Binary Transformers for Multivariate Time Series Modeling	Aug 9, 2023	Anomaly DetectionTime Series	—Unverified
Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection	Aug 9, 2023	Anomaly DetectionDimensionality Reduction	—Unverified
Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection	Aug 9, 2023	Anomaly DetectionDefect Detection	—Unverified
A Deep-Learning Method Using Auto-encoder and Generative Adversarial Network for Anomaly Detection on Ancient Stone Stele Surfaces	Aug 8, 2023	Anomaly DetectionGenerative Adversarial Network	—Unverified
Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization	Aug 8, 2023	Anomaly DetectionBayesian Optimization	—Unverified
Implementing Immune Repertoire Models Using Weighted Finite State Machines	Aug 7, 2023	Anomaly Detection	—Unverified
Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques	Aug 6, 2023	Anomaly DetectionTime Series	—Unverified
Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph	Aug 5, 2023	Anomaly DetectionFraud Detection	CodeCode Available
Anomaly Detection in Global Financial Markets with Graph Neural Networks and Nonextensive Entropy	Aug 5, 2023	Anomaly Detection	—Unverified
Synthetic outlier generation for anomaly detection in autonomous driving	Aug 4, 2023	Anomaly DetectionAutonomous Driving	—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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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