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 1611–1620 of 4856 papers

Title	Date	Tasks	Status	Hype
Video Anomaly Detection using GAN	Nov 23, 2023	Anomaly DetectionEvent Detection	—Unverified	0
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators	Nov 22, 2023	Anomaly Detection	—Unverified	0
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling	Nov 21, 2023	Anomaly DetectionTime Series	CodeCode Available	1
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET	Nov 20, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly Detector	Nov 20, 2023	Anomaly DetectionBenchmarking	CodeCode Available	1
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation	Nov 20, 2023	Anomaly DetectionData Augmentation	CodeCode Available	1
Correlated Attention in Transformers for Multivariate Time Series	Nov 20, 2023	Anomaly DetectionImputation	—Unverified	0
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection	Nov 20, 2023	Anomaly Detection	CodeCode Available	0
Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution	Nov 19, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI	Nov 19, 2023	Anomaly DetectionDiversity	—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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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