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 2831–2840 of 4856 papers

Title	Date	Tasks	Status
A Deep Learning Anomaly Detection Method in Textual Data	Nov 25, 2022	Anomaly DetectionDeep Learning	—Unverified
Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets	Nov 24, 2022	Anomaly DetectionMeta-Learning	—Unverified
Learning Invariant Rules from Data for Interpretable Anomaly Detection	Nov 24, 2022	Anomaly DetectionDecision Making	CodeCode Available
Detecting Anomalies using Generative Adversarial Networks on Images	Nov 24, 2022	Anomaly DetectionDecoder	—Unverified
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD	Nov 23, 2022	Anomaly DetectionOutlier Detection	CodeCode Available
FRE: A Fast Method For Anomaly Detection And Segmentation	Nov 23, 2022	Anomaly DetectionCPU	—Unverified
An Adaptive Event-based Data Converter for Always-on Biomedical Applications at the Edge	Nov 23, 2022	Anomaly Detection	—Unverified
Corn Yield Prediction based on Remotely Sensed Variables Using Variational Autoencoder and Multiple Instance Regression	Nov 23, 2022	Anomaly DetectionCrop Yield Prediction	—Unverified
A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRI	Nov 22, 2022	Anomaly DetectionMetric Learning	CodeCode Available
MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series	Nov 22, 2022	Anomaly DetectionGraph Anomaly Detection	—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	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