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 4041–4050 of 4856 papers

Title	Date	Tasks	Status
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection	Oct 30, 2023	Anomaly DetectionFederated Learning	—Unverified
Privacy-Preserving Hybrid Ensemble Model for Network Anomaly Detection: Balancing Security and Data Protection	Feb 13, 2025	Anomaly DetectionPrivacy Preserving	—Unverified
Privacy-Preserving Video Anomaly Detection: A Survey	Nov 21, 2024	Anomaly DetectionEthics	—Unverified
Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videos	Dec 20, 2022	Anomaly DetectionSemantic Segmentation	—Unverified
Probabilistic Graphs for Sensor Data-driven Modelling of Power Systems at Scale	Nov 18, 2018	Anomaly DetectionState Estimation	—Unverified
Probabilistic Robust Autoencoders for Outlier Detection	Oct 1, 2021	Anomaly DetectionOutlier Detection	—Unverified
Probabilistic Sampling-Enhanced Temporal-Spatial GCN: A Scalable Framework for Transaction Anomaly Detection in Ethereum Networks	Sep 29, 2023	Anomaly DetectionTemporal Sequences	—Unverified
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks	Feb 3, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images	Dec 23, 2023	Anomaly DetectionArtifact Detection	CodeCode Available
Exploring the Potential of Multi-Modal AI for Driving Hazard Prediction	Oct 7, 2023	Anomaly DetectionVisual Abductive Reasoning	CodeCode Available

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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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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