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 1641–1650 of 4856 papers

Title	Date	Tasks	Status	Hype
k-Parameter Approach for False In-Season Anomaly Suppression in Daily Time Series Anomaly Detection	Nov 10, 2023	Anomaly DetectionPosition	—Unverified	0
GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps	Nov 10, 2023	Anomaly DetectionDecision Making	—Unverified	0
Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network	Nov 9, 2023	Anomaly DetectionFault Detection	—Unverified	0
RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information	Nov 9, 2023	Anomaly DetectionRetrieval	CodeCode Available	1
Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?	Nov 8, 2023	Anomaly Detection	—Unverified	0
The voraus-AD Dataset for Anomaly Detection in Robot Applications	Nov 8, 2023	Anomaly DetectionBenchmarking	CodeCode Available	1
A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders	Nov 7, 2023	Anomaly DetectionDecoder	—Unverified	0
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter	Nov 7, 2023	Anomaly Detection	—Unverified	0
Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset	Nov 7, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	0
A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection	Nov 6, 2023	Anomaly DetectionGenerative Adversarial Network	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