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 3391–3400 of 4856 papers

Title	Date	Tasks	Status
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization	Oct 9, 2021	Anomaly DetectionContrastive Learning	—Unverified
Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection	Oct 8, 2021	Anomaly DetectionTime Series	—Unverified
Minimal-Configuration Anomaly Detection for IIoT Sensors	Oct 8, 2021	Anomaly DetectionFeature Engineering	—Unverified
Anomaly Detection in Beehives: An Algorithm Comparison	Oct 8, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Differential Anomaly Detection for Facial Images	Oct 7, 2021	Anomaly DetectionFace Recognition	—Unverified
Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning	Oct 7, 2021	Anomaly DetectionDeep Learning	—Unverified
Generative Pre-Trained Transformer for Cardiac Abnormality Detection	Oct 7, 2021	Anomaly DetectionClassification	—Unverified
AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning	Oct 7, 2021	Anomaly DetectionAnomaly Segmentation	—Unverified
A Uniform Framework for Anomaly Detection in Deep Neural Networks	Oct 6, 2021	Adversarial AttackAnomaly Detection	CodeCode Available
Generative Optimization Networks for Memory Efficient Data Generation	Oct 6, 2021	Anomaly DetectionTime Series	CodeCode Available

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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