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 1991–2000 of 4856 papers

Title	Date	Tasks	Status
Language Models Meet Anomaly Detection for Better Interpretability and Generalizability	Apr 11, 2024	Anomaly DetectionLanguage Modelling	CodeCode Available
Context-aware Video Anomaly Detection in Long-Term Datasets	Apr 11, 2024	Anomaly DetectionContrastive Learning	—Unverified
Anomaly Detection in Power Grids via Context-Agnostic Learning	Apr 11, 2024	Anomaly Detection	—Unverified
3D-CSAD: Untrained 3D Anomaly Detection for Complex Manufacturing Surfaces	Apr 11, 2024	3D Anomaly DetectionAnomaly Detection	—Unverified
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability	Apr 9, 2024	Anomaly DetectionOutlier Detection	—Unverified
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey	Apr 8, 2024	Adversarial RobustnessAnomaly Detection	—Unverified
EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection	Apr 8, 2024	Anomaly Detection	—Unverified
TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis	Apr 7, 2024	Anomaly DetectionContrastive Learning	—Unverified
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning	Apr 7, 2024	Anomaly DetectionAnomaly Localization	—Unverified
A Deep Look Into -- Automated Lung X-Ray Abnormality Detection System	Apr 6, 2024	Anomaly Detection	—Unverified

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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