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 3931–3940 of 4856 papers

Title	Date	Tasks	Status	Hype
Transformation Based Deep Anomaly Detection in Astronomical Images	May 15, 2020	Anomaly DetectionDimensionality Reduction	—Unverified	0
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs	May 15, 2020	Anomaly DetectionGraph Neural Network	CodeCode Available	1
A Weighted Mutual k-Nearest Neighbour for Classification Mining	May 14, 2020	Anomaly DetectionClassification	CodeCode Available	0
Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks	May 14, 2020	Anomaly DetectionClassification	—Unverified	0
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms	May 14, 2020	Anomaly DetectionDeep Learning	—Unverified	0
Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts	May 12, 2020	Anomaly DetectionContour Detection	—Unverified	0
Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End Optimization	May 12, 2020	Anomaly DetectionMetric Learning	CodeCode Available	1
Integrated Methodology to Cognitive Network \& Slice Management in Virtualized 5G Networks	May 11, 2020	Anomaly DetectionManagement	—Unverified	0
Personalized Early Stage Alzheimer's Disease Detection: A Case Study of President Reagan's Speeches	May 8, 2020	Alzheimer's Disease DetectionAnomaly Detection	—Unverified	0
Learning Generalized Spoof Cues for Face Anti-spoofing	May 8, 2020	Anomaly DetectionDiversity	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	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