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 3221–3230 of 4856 papers

Title	Date	Tasks	Status
Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection	Feb 25, 2022	Anomaly DetectionTime Series	—Unverified
Do autoencoders need a bottleneck for anomaly detection?	Feb 25, 2022	Anomaly Detection	—Unverified
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations	Feb 24, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
A spectral-spatial fusion anomaly detection method for hyperspectral imagery	Feb 24, 2022	Anomaly Detection	—Unverified
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors	Feb 23, 2022	3D Anomaly Detection3D Anomaly Detection and Segmentation	—Unverified
Augmentation based unsupervised domain adaptation	Feb 23, 2022	Anomaly DetectionDomain Adaptation	—Unverified
Deep Graph Learning for Anomalous Citation Detection	Feb 23, 2022	Anomaly DetectionCPU	—Unverified
ML-based Anomaly Detection in Optical Fiber Monitoring	Feb 23, 2022	Anomaly Detection	—Unverified
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization	Feb 22, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants	Feb 21, 2022	Anomaly DetectionModel Selection	—Unverified

Show:10 25 50

← PrevPage 323 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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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