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 701–710 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection	Apr 16, 2025	Anomaly DetectionLarge Language Model	CodeCode Available	1	5
Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopes	Jan 28, 2024	Anomaly DetectionExplainable artificial intelligence	CodeCode Available	1	5
Graph Neural Network-Based Anomaly Detection for River Network Systems	Apr 19, 2023	Anomaly DetectionBenchmarking	CodeCode Available	1	5
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis	Feb 20, 2023	Anomaly DetectionDeep Reinforcement Learning	CodeCode Available	1	5
A Survey of World Models for Autonomous Driving	Jan 20, 2025	Anomaly DetectionAutonomous Driving	CodeCode Available	1	5
Graph Neural Networks based Log Anomaly Detection and Explanation	Jul 2, 2023	Anomaly DetectionGraph Neural Network	CodeCode Available	1	5
Continuous Memory Representation for Anomaly Detection	Feb 28, 2024	Anomaly Detection	CodeCode Available	1	5
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data	Sep 15, 2020	Anomaly DetectionDeep Reinforcement Learning	CodeCode Available	1	5
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs	May 9, 2022	AnatomyAnomaly Detection	CodeCode Available	1	5
Anomaly Detection in Emails using Machine Learning and Header Information	Mar 19, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1	5

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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