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 741–750 of 4856 papers

Title	Date	Tasks	Status	Hype
Detection of Emerging Infectious Diseases in Lung CT based on Spatial Anomaly Patterns	Oct 25, 2024	Anomaly Detection	—Unverified	0
Temporal Convolution-based Hybrid Model Approach with Representation Learning for Real-Time Acoustic Anomaly Detection	Oct 25, 2024	Anomaly DetectionRepresentation Learning	—Unverified	0
Context-Aware Trajectory Anomaly Detection	Oct 24, 2024	Anomaly DetectionDecision Making	—Unverified	0
Harnessing PU Learning for Enhanced Cloud-based DDoS Detection: A Comparative Analysis	Oct 24, 2024	Anomaly Detection	—Unverified	0
Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images	Oct 24, 2024	Anomaly DetectionDiagnostic	—Unverified	0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy	Oct 24, 2024	Active LearningAnomaly Detection	—Unverified	0
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. Performance	Oct 24, 2024	Anomaly DetectionDe-identification	CodeCode Available	0
Graph Pre-Training Models Are Strong Anomaly Detectors	Oct 24, 2024	Anomaly DetectionGraph Anomaly Detection	—Unverified	0
Multi-scale feature reconstruction network for industrial anomaly detection	Oct 23, 2024	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Coniferest: a complete active anomaly detection framework	Oct 22, 2024	Anomaly DetectionOutlier Detection	CodeCode Available	1

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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