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 4311–4320 of 4856 papers

Title	Date	Tasks	Status	Hype
Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches	May 11, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization	May 10, 2019	Anomaly DetectionRetrieval	CodeCode Available	0
Anomaly Detection in Images	May 9, 2019	Anomaly DetectionSupervised Anomaly Detection	—Unverified	0
1D Convolutional Neural Networks and Applications: A Survey	May 9, 2019	Anomaly DetectionFault Detection	—Unverified	0
Deep Anomaly Detection on Attributed Networks	May 2, 2019	Anomaly DetectionAttribute	CodeCode Available	1
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing	May 1, 2019	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
UaiNets: From Unsupervised to Active Deep Anomaly Detection	May 1, 2019	Anomaly DetectionDeep Learning	—Unverified	0
EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models	May 1, 2019	Anomaly DetectionNovelty Detection	—Unverified	0
Consistency-based anomaly detection with adaptive multiple-hypotheses predictions	May 1, 2019	Anomaly DetectionDiversity	CodeCode Available	0
Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction	Apr 30, 2019	Anomaly DetectionAutonomous Vehicles	—Unverified	0

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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