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 4481–4490 of 4856 papers

Title	Date	Tasks	Status
Does Your Phone Know Your Touch?	Sep 10, 2018	Anomaly DetectionPosition	—Unverified
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data	Sep 8, 2018	Anomaly DetectionFeature Engineering	—Unverified
Multi-level hypothesis testing for populations of heterogeneous networks	Sep 7, 2018	Anomaly DetectionTwo-sample testing	—Unverified
Anomaly Detection in the Presence of Missing Values	Sep 5, 2018	Anomaly DetectionImputation	—Unverified
An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection	Sep 1, 2018	Anomaly DetectionRhythm	—Unverified
DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders	Aug 30, 2018	Anomaly Detection	CodeCode Available
AAD: Adaptive Anomaly Detection through traffic surveillance videos	Aug 29, 2018	Action DetectionActivity Detection	—Unverified
Surface Defect Saliency of Magnetic Tile	Aug 24, 2018	Anomaly DetectionDefect Detection	CodeCode Available
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN	Aug 23, 2018	Anomaly DetectionData Augmentation	—Unverified
Enhanced network anomaly detection based on deep neural networks	Aug 17, 2018	Anomaly DetectionBIG-bench Machine Learning	—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