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 1771–1780 of 4856 papers

Title	Date	Tasks	Status	Hype
mixed attention auto encoder for multi-class industrial anomaly detection	Sep 22, 2023	Anomaly DetectionMulti-class Anomaly Detection	—Unverified	0
Ano-SuPs: Multi-size anomaly detection for manufactured products by identifying suspected patches	Sep 20, 2023	Anomaly Detection	—Unverified	0
Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing	Sep 20, 2023	Anomaly DetectionFault Detection	—Unverified	0
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward	Sep 19, 2023	Anomaly DetectionPhysics-informed machine learning	—Unverified	0
Semi-automatic staging area for high-quality structured data extraction from scientific literature	Sep 19, 2023	Anomaly Detection	CodeCode Available	0
LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data	Sep 19, 2023	Anomaly DetectionAutonomous Driving	CodeCode Available	1
Two-stage coarse-to-fine image anomaly segmentation and detection model	Sep 19, 2023	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter	Sep 18, 2023	Anomaly Detection	—Unverified	0
Active anomaly detection based on deep one-class classification	Sep 18, 2023	Active LearningAnomaly Detection	—Unverified	0
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination	Sep 18, 2023	Anomaly DetectionOne-Class Classification	—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