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 4601–4610 of 4856 papers

Title	Date	Tasks	Status	Hype
Anomaly Detection with Generative Adversarial Networks	Jan 1, 2018	Anomaly Detection	—Unverified	0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection	Jan 1, 2018	Anomaly DetectionChange Point Detection	—Unverified	0
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection	Jan 1, 2018	Anomaly DetectionDensity Estimation	CodeCode Available	0
End-to-End Abnormality Detection in Medical Imaging	Jan 1, 2018	Anomaly DetectionComputed Tomography (CT)	—Unverified	0
Future Frame Prediction for Anomaly Detection -- A New Baseline	Dec 28, 2017	Anomaly DetectionOptical Flow Estimation	CodeCode Available	1
Overcomplete Frame Thresholding for Acoustic Scene Analysis	Dec 25, 2017	Action DetectionActivity Detection	—Unverified	0
Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things	Dec 18, 2017	Anomaly DetectionEdge-computing	—Unverified	0
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time	Dec 18, 2017	Anomaly DetectionGeneral Classification	—Unverified	0
Outlier Detection by Consistent Data Selection Method	Dec 12, 2017	Anomaly DetectionClustering	—Unverified	0
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs	Dec 11, 2017	Anomaly DetectionSpecificity	CodeCode Available	1

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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