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 2931–2940 of 4856 papers

Title	Date	Tasks	Status
RePAD: Real-time Proactive Anomaly Detection for Time Series	Jan 24, 2020	Anomaly DetectionFraud Detection	—Unverified
Representation Learning for Resource Usage Prediction	Feb 2, 2018	Anomaly DetectionPrediction	—Unverified
Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challenge	Jul 20, 2023	Anomaly DetectionRepresentation Learning	—Unverified
Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs	Aug 25, 2023	Anomaly DetectionKnowledge Graphs	—Unverified
[Reproducibility Report] Explainable Deep One-Class Classification	Jun 6, 2022	Anomaly DetectionClassification	—Unverified
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series	Apr 5, 2020	Anomaly DetectionIntrusion Detection	—Unverified
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers	Jul 18, 2022	Anomaly DetectionDiagnostic	—Unverified
Research and application of Transformer based anomaly detection model: A literature review	Feb 14, 2024	Anomaly Detection	—Unverified
Research on Anomaly Detection Methods Based on Diffusion Models	May 8, 2025	Anomaly DetectionFault Diagnosis	—Unverified
Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models	Apr 22, 2025	Anomaly DetectionComputational Efficiency	—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