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 1221–1230 of 4856 papers

Title	Date	Tasks	Status	Hype
Generic Multi-modal Representation Learning for Network Traffic Analysis	May 4, 2024	Anomaly DetectionFeature Engineering	—Unverified	0
Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?	May 4, 2024	Anomaly DetectionBenchmarking	CodeCode Available	1
Systematic Review: Anomaly Detection in Connected and Autonomous Vehicles	May 4, 2024	Anomaly DetectionArticles	—Unverified	0
A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System	May 4, 2024	Anomaly Detection	—Unverified	0
An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems	May 3, 2024	Anomaly DetectionDecoder	—Unverified	0
CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly Detection	May 3, 2024	Anomaly DetectionData Augmentation	CodeCode Available	0
Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection	May 3, 2024	Anomaly DetectionAttribute	CodeCode Available	1
Subgraph2vec: A random walk-based algorithm for embedding knowledge graphs	May 3, 2024	Anomaly DetectionDecision Making	—Unverified	0
Interpretable Data-driven Anomaly Detection in Industrial Processes with ExIFFI	May 2, 2024	Anomaly DetectionComputational Efficiency	—Unverified	0
Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving	May 2, 2024	Anomaly DetectionAutonomous Driving	—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