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 1141–1150 of 4856 papers

Title	Date	Tasks	Status
iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning	Apr 6, 2025	Anomaly DetectionMeta-Learning	—Unverified
AttackLLM: LLM-based Attack Pattern Generation for an Industrial Control System	Apr 5, 2025	Anomaly Detection	—Unverified
Foundation Models for Time Series: A Survey	Apr 5, 2025	Anomaly DetectionSurvey	—Unverified
Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly Detection	Apr 5, 2025	Anomaly DetectionDescriptive	CodeCode Available
Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection	Apr 4, 2025	Anomaly DetectionAnomaly Localization	CodeCode Available
Improving log-based anomaly detection through learned adaptive filter	Apr 3, 2025	Anomaly DetectionDeep Reinforcement Learning	—Unverified
Analytical Discovery of Manifold with Machine Learning	Apr 3, 2025	Anomaly DetectionComputational Efficiency	—Unverified
VISTA: Unsupervised 2D Temporal Dependency Representations for Time Series Anomaly Detection	Apr 3, 2025	Anomaly DetectionTime Series	—Unverified
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning	Apr 3, 2025	Active LearningAnomaly Detection	—Unverified
CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection	Apr 3, 2025	Anomaly DetectionGraph Anomaly Detection	—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