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 2801–2810 of 4856 papers

Title	Date	Tasks	Status
Continual Learning for Anomaly Detection in Surveillance Videos	Apr 15, 2020	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
Continual learning on deployment pipelines for Machine Learning Systems	Dec 5, 2022	Anomaly DetectionContinual Learning	—Unverified
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning	Nov 13, 2024	Anomaly DetectionEdge-computing	—Unverified
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions	Jun 6, 2024	Anomaly DetectionDomain Adaptation	—Unverified
Learning Informative Health Indicators Through Unsupervised Contrastive Learning	Aug 28, 2022	Anomaly DetectionContrastive Learning	—Unverified
Contrastive Learning for Time Series on Dynamic Graphs	Sep 21, 2022	Activity RecognitionAnomaly Detection	—Unverified
Contrastive Predictive Coding for Anomaly Detection	Jul 16, 2021	Anomaly DetectionAnomaly Segmentation	—Unverified
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data	Feb 8, 2022	Anomaly DetectionTime Series	—Unverified
Contrastive-Regularized U-Net for Video Anomaly Detection	Apr 11, 2023	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
Contrastive Structured Anomaly Detection for Gaussian Graphical Models	May 2, 2016	Anomaly Detection	—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