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 2431–2440 of 4856 papers

Title	Date	Tasks	Status	Hype
Normal Reference Attention and Defective Feature Perception Network for Surface Defect Detection	Nov 18, 2022	Anomaly DetectionDefect Detection	—Unverified	0
Rare Yet Popular: Evidence and Implications from Labeled Datasets for Network Anomaly Detection	Nov 18, 2022	Anomaly Detection	—Unverified	0
Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation	Nov 17, 2022	Anomaly DetectionClustering	—Unverified	0
Dynamic Interactional And Cooperative Network For Shield Machine	Nov 17, 2022	Anomaly Detection	—Unverified	0
SpectNet : End-to-End Audio Signal Classification Using Learnable Spectrograms	Nov 17, 2022	Acoustic Scene ClassificationAnomaly Detection	CodeCode Available	0
Heart Abnormality Detection from Heart Sound Signals using MFCC Feature and Dual Stream Attention Based Network	Nov 17, 2022	Anomaly Detection	—Unverified	0
Anomaly Detection via Multi-Scale Contrasted Memory	Nov 16, 2022	Anomaly DetectionContrastive Learning	—Unverified	0
On the Connection of Generative Models and Discriminative Models for Anomaly Detection	Nov 16, 2022	Anomaly Detection	—Unverified	0
Are we certain it's anomalous?	Nov 16, 2022	Anomaly DetectionTime Series	CodeCode Available	1
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection	Nov 15, 2022	Anomaly DetectionDensity Estimation	CodeCode Available	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