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 3981–3990 of 4856 papers

Title	Date	Tasks	Status
Patch-wise Auto-Encoder for Visual Anomaly Detection	Aug 1, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Patchwise Sparse Dictionary Learning from pre-trained Neural Network Activation Maps for Anomaly Detection in Images	Sep 29, 2021	Anomaly DetectionDictionary Learning	—Unverified
Pattern-Based Time-Series Risk Scoring for Anomaly Detection and Alert Filtering -- A Predictive Maintenance Case Study	May 24, 2024	Anomaly DetectionFault Detection	—Unverified
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation	Oct 29, 2021	Anomaly DetectionAnomaly Localization	—Unverified
Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection	Nov 18, 2022	Anomaly DetectionDecoder	—Unverified
Pediatric Otoscopy Video Screening with Shift Contrastive Anomaly Detection	Oct 25, 2021	Anomaly DetectionDiagnostic	—Unverified
Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud	Dec 11, 2019	Anomaly Detectioncounterfactual	—Unverified
Perfect density models cannot guarantee anomaly detection	Dec 7, 2020	Active LearningAnomaly Detection	—Unverified
Performance Analysis of a Foreground Segmentation Neural Network Model	May 26, 2021	Anomaly DetectionDecoder	—Unverified
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection	Aug 22, 2023	Anomaly DetectionIntrusion 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