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 3621–3630 of 4856 papers

Title	Date	Tasks	Status
Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes	Feb 10, 2025	Anomaly Detection	—Unverified
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET	Nov 20, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle	Oct 17, 2023	Anomaly DetectionDecision Making	—Unverified
Leveraging Large Self-Supervised Time-Series Models for Transferable Diagnosis in Cross-Aircraft Type Bleed Air System	Apr 12, 2025	Anomaly DetectionDiagnostic	—Unverified
Leveraging Registers in Vision Transformers for Robust Adaptation	Jan 8, 2025	Anomaly DetectionObject Discovery	—Unverified
Leveraging Siamese Networks for One-Shot Intrusion Detection Model	Jun 27, 2020	Anomaly DetectionIntrusion Detection	—Unverified
Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection	Dec 7, 2024	Anomaly DetectionComputational Efficiency	—Unverified
Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection	Sep 24, 2024	Anomaly Detection	—Unverified
LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing	May 20, 2023	Anomaly DetectionCPU	—Unverified
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning	Aug 16, 2017	Anomaly DetectionData Compression	—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	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