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 1871–1880 of 4856 papers

Title	Date	Tasks	Status	Hype
Out-of-Distribution Detection for Monocular Depth Estimation	Aug 11, 2023	Anomaly DetectionDecoder	CodeCode Available	1
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation	Aug 11, 2023	Anomaly DetectionData Poisoning	—Unverified	0
A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions	Aug 11, 2023	Anomaly DetectionCPU	CodeCode Available	0
Deep generative models for unsupervised delamination detection using guided waves	Aug 10, 2023	Anomaly DetectionStructural Health Monitoring	CodeCode Available	0
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving	Aug 10, 2023	Anomaly DetectionAutonomous Driving	—Unverified	0
Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection	Aug 9, 2023	Anomaly DetectionDimensionality Reduction	—Unverified	0
Sparse Binary Transformers for Multivariate Time Series Modeling	Aug 9, 2023	Anomaly DetectionTime Series	—Unverified	0
Multi-Class Deep SVDD: Anomaly Detection Approach in Astronomy with Distinct Inlier Categories	Aug 9, 2023	Anomaly DetectionAstronomy	CodeCode Available	1
Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection	Aug 9, 2023	Anomaly DetectionDefect Detection	—Unverified	0
Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization	Aug 8, 2023	Anomaly DetectionBayesian Optimization	—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	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