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 1151–1160 of 4856 papers

Title	Date	Tasks	Status	Hype
Anomaly Detection Using Normalizing Flow-Based Density Estimation and Synthetic Defect Classification	May 28, 2024	Anomaly DetectionClassification	CodeCode Available	0
Anomaly detection for the identification of volcanic unrest in satellite imagery	May 28, 2024	Anomaly Detection	—Unverified	0
When and How Does In-Distribution Label Help Out-of-Distribution Detection?	May 28, 2024	Anomaly DetectionOut-of-Distribution Detection	CodeCode Available	0
Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs	May 28, 2024	Anomaly DetectionGraph Learning	CodeCode Available	1
SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection	May 27, 2024	Anomaly DetectionGraph Learning	—Unverified	0
ARC: A Generalist Graph Anomaly Detector with In-Context Learning	May 27, 2024	Anomaly DetectionARC	CodeCode Available	1
Hawk: Learning to Understand Open-World Video Anomalies	May 27, 2024	Anomaly DetectionQuestion Answering	CodeCode Available	3
A Study on Unsupervised Anomaly Detection and Defect Localization using Generative Model in Ultrasonic Non-Destructive Testing	May 26, 2024	Anomaly DetectionDefect Detection	—Unverified	0
KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation	May 26, 2024	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection	May 25, 2024	Anomaly DetectionFederated 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	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