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 1291–1300 of 4856 papers

Title	Date	Tasks	Status	Hype
Time topological analysis of EEG using signature theory	Apr 6, 2024	Anomaly DetectionEEG	—Unverified	0
A Deep Look Into -- Automated Lung X-Ray Abnormality Detection System	Apr 6, 2024	Anomaly Detection	—Unverified	0
MedIAnomaly: A comparative study of anomaly detection in medical images	Apr 6, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available	2
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold	Apr 5, 2024	Anomaly Detection	—Unverified	0
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection	Apr 5, 2024	Anomaly DetectionDictionary Learning	CodeCode Available	0
Test Time Training for Industrial Anomaly Segmentation	Apr 4, 2024	Anomaly DetectionAnomaly Segmentation	—Unverified	0
Learning with errors based dynamic encryption that discloses residue signal for anomaly detection	Apr 3, 2024	Anomaly Detection	—Unverified	0
Foundation Models for Structural Health Monitoring	Apr 3, 2024	Anomaly DetectionKnowledge Distillation	CodeCode Available	0
Transfer learning applications for anomaly detection in wind turbines	Apr 3, 2024	Anomaly DetectionDecoder	—Unverified	0
TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly Detection	Apr 3, 2024	Anomaly DetectionData Augmentation	CodeCode Available	0

Show:10 25 50

← PrevPage 130 of 486Next →

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