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 1181–1190 of 4856 papers

Title	Date	Tasks	Status	Hype
Automated Anomaly Detection on European XFEL Klystrons	May 20, 2024	Anomaly DetectionDimensionality Reduction	—Unverified	0
PATE: Proximity-Aware Time series anomaly Evaluation	May 20, 2024	Anomaly DetectionDecision Making	CodeCode Available	1
Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays	May 20, 2024	Anomaly DetectionPosition	CodeCode Available	1
SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection	May 18, 2024	Anomaly DetectionTime Series	CodeCode Available	1
MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection	May 18, 2024	Anomaly DetectionDecision Making	CodeCode Available	2
ECATS: Explainable-by-design concept-based anomaly detection for time series	May 17, 2024	Anomaly DetectionTime Series	—Unverified	0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks	May 17, 2024	Anomaly DetectionData Augmentation	CodeCode Available	0
A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text	May 16, 2024	Anomaly DetectionBenchmarking	—Unverified	0
Networking Systems for Video Anomaly Detection: A Tutorial and Survey	May 16, 2024	Anomaly DetectionEdge-computing	CodeCode Available	1
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection	May 16, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available	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