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 1811–1820 of 4856 papers

Title	Date	Tasks	Status	Hype
Anomaly detection with semi-supervised classification based on risk estimators	Sep 1, 2023	Anomaly DetectionOne-Class Classification	—Unverified	0
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market	Aug 31, 2023	Anomaly DetectionFraud Detection	—Unverified	0
Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter	Aug 31, 2023	Anomaly Detection	—Unverified	0
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI	Aug 30, 2023	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Demo: A Digital Twin of the 5G Radio Access Network for Anomaly Detection Functionality	Aug 30, 2023	Anomaly Detection	—Unverified	0
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization	Aug 30, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Classification of Anomalies in Telecommunication Network KPI Time Series	Aug 30, 2023	Anomaly ClassificationAnomaly Detection	—Unverified	0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification	Aug 29, 2023	Anomaly DetectionClassification	—Unverified	0
MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection	Aug 29, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	1
A Comprehensive Augmentation Framework for Anomaly Detection	Aug 29, 2023	Anomaly DetectionData Augmentation	—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	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