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 951–960 of 4856 papers

Title	Date	Tasks	Status
Abnormality Detection in Musculoskeletal Radiographs with Convolutional Neural Networks(Ensembles) and Performance Optimization	Aug 6, 2019	Anomaly DetectionDiagnostic	—Unverified
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection	Jul 20, 2021	Anomaly DetectionDeep Learning	—Unverified
Anomaly Detection for Multivariate Time Series of Exotic Supernovae	Oct 21, 2020	Anomaly DetectionTime Series	—Unverified
A GAN-based data poisoning framework against anomaly detection in vertical federated learning	Jan 17, 2024	Anomaly DetectionData Poisoning	—Unverified
AGAD: Adversarial Generative Anomaly Detection	Apr 9, 2023	Anomaly DetectionDiversity	—Unverified
Anomaly Detection for Industrial Big Data	Apr 9, 2018	Anomaly DetectionTime Series	—Unverified
Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review	Jan 20, 2025	Anomaly Detection	—Unverified
Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?	Jan 27, 2025	Anomaly ClassificationAnomaly Detection	—Unverified
Anomaly Detection for Incident Response at Scale	Apr 24, 2024	Anomaly DetectionDrift Detection	—Unverified
Anomaly Detection for imbalanced datasets with Deep Generative Models	Nov 2, 2018	Anomaly DetectionMedical Image Analysis	—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	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