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 3361–3370 of 4856 papers

Title	Date	Tasks	Status	Hype
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice	May 30, 2021	Adversarial AttackAnomaly Detection	CodeCode Available	0
Shell Theory: A Statistical Model of Reality	May 28, 2021	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	0
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider	May 28, 2021	Anomaly DetectionDensity Estimation	CodeCode Available	0
A Survey on Anomaly Detection for Technical Systems using LSTM Networks	May 28, 2021	Anomaly DetectionSurvey	—Unverified	0
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms	May 26, 2021	Anomaly DetectionNovelty Detection	—Unverified	0
Performance Analysis of a Foreground Segmentation Neural Network Model	May 26, 2021	Anomaly DetectionDecoder	—Unverified	0
CI-dataset and DetDSCI methodology for detecting too small and too large critical infrastructures in satellite images: Airports and electrical substations as case study	May 25, 2021	Anomaly DetectionChange Detection	—Unverified	0
Finite sample guarantees for quantile estimation: An application to detector threshold tuning	May 25, 2021	Anomaly Detection	CodeCode Available	0
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data	May 25, 2021	Anomaly DetectionConformal Prediction	CodeCode Available	1
Deep Visual Anomaly detection with Negative Learning	May 24, 2021	Anomaly DetectionHallucination	—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