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 2291–2300 of 4856 papers

Title	Date	Tasks	Status
Coincident Learning for Unsupervised Anomaly Detection	Jan 26, 2023	Anomaly DetectionTime Series	—Unverified
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection	Feb 29, 2024	Anomaly Detection	—Unverified
Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology	Jan 28, 2024	Anomaly DetectionFault Detection	—Unverified
CoDetect: Financial Fraud Detection With Anomaly Feature Detection	Mar 26, 2018	Anomaly DetectionFraud Detection	—Unverified
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts	Nov 26, 2024	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches	Oct 20, 2023	Anomaly DetectionComputer Security	—Unverified
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection	Dec 31, 2024	Anomaly DetectionAttribute	—Unverified
Operational range bounding of spectroscopy models with anomaly detection	Aug 5, 2024	Anomaly Detection	—Unverified
Anomaly Detection Models for IoT Time Series Data	Nov 30, 2018	Anomaly DetectionTime Series	—Unverified
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection	Jan 19, 2023	Anomaly DetectionClustering	—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