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 3901–3910 of 4856 papers

Title	Date	Tasks	Status
Uncertainty-Aware Deep Classifiers using Generative Models	Jun 7, 2020	Anomaly Detection	—Unverified
Hybrid Model for Anomaly Detection on Call Detail Records by Time Series Forecasting	Jun 7, 2020	Anomaly DetectionTime Series	—Unverified
LSTM-based Anomaly Detection for Non-linear Dynamical System	Jun 5, 2020	Anomaly DetectionPrediction	—Unverified
Anomaly Detection with Domain Adaptation	Jun 5, 2020	Anomaly DetectionDomain Adaptation	—Unverified
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning	Jun 5, 2020	Anomaly Detectionimage-classification	—Unverified
Unsupervised Abnormality Detection Using Heterogeneous Autonomous Systems	Jun 5, 2020	Anomaly DetectionAutonomous Vehicles	—Unverified
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images	Jun 5, 2020	Anomaly DetectionData Augmentation	—Unverified
Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path Signature	Jun 5, 2020	Anomaly DetectionTime Series	CodeCode Available
Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environments	Jun 4, 2020	Anomaly DetectionDiagnostic	—Unverified
Anomaly Detection with Tensor Networks	Jun 3, 2020	Anomaly DetectionComputational Efficiency	—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	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