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 1421–1430 of 4856 papers

Title	Date	Tasks	Status
Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation	Dec 18, 2024	Anomaly Detection	CodeCode Available
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection	Dec 17, 2024	Anomaly DetectionBackdoor Attack	—Unverified
PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection	Dec 17, 2024	3D Anomaly DetectionAnomaly Detection	—Unverified
Synthetic Data Generation for Anomaly Detection on Table Grapes	Dec 17, 2024	Anomaly DetectionClassification	CodeCode Available
Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation	Dec 16, 2024	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Are Large Language Models Useful for Time Series Data Analysis?	Dec 16, 2024	Anomaly DetectionDecision Making	—Unverified
F-RBA: A Federated Learning-based Framework for Risk-based Authentication	Dec 16, 2024	Anomaly DetectionFeature Engineering	—Unverified
AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP	Dec 16, 2024	Anomaly DetectionDistributed Computing	—Unverified
Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational Autoencoders	Dec 16, 2024	Anomaly Detection	CodeCode Available
Learning Set Functions with Implicit Differentiation	Dec 15, 2024	Anomaly DetectionProduct Recommendation	—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