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 1141–1150 of 4856 papers

Title	Date	Tasks	Status
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection	Apr 14, 2023	Anomaly DetectionDensity Estimation	—Unverified
A Taxonomy of Anomalies in Log Data	Nov 26, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks	May 14, 2020	Anomaly DetectionClassification	—Unverified
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization	Jan 10, 2019	Anomaly DetectionData Augmentation	—Unverified
A task of anomaly detection for a smart satellite Internet of things system	Mar 21, 2024	Anomaly DetectionGenerative Adversarial Network	—Unverified
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference	Jan 23, 2023	Anomaly DetectionImage Generation	—Unverified
Anomaly Detection and Classification in Knowledge Graphs	Dec 6, 2024	Anomaly DetectionClassification	—Unverified
Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)	Jan 16, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
ATAC-Net: Zoomed view works better for Anomaly Detection	Jun 20, 2024	Anomaly Detection	—Unverified
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions	Oct 28, 2024	Anomaly DetectionFairness	—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	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