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 3751–3760 of 4856 papers

Title	Date	Tasks	Status
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation	Jun 27, 2024	Anomaly DetectionGraph Generation	—Unverified
mixed attention auto encoder for multi-class industrial anomaly detection	Sep 22, 2023	Anomaly DetectionMulti-class Anomaly Detection	—Unverified
Mixture Proportion Estimation via Kernel Embedding of Distributions	Mar 8, 2016	Anomaly DetectionWeakly-supervised Learning	—Unverified
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network	Mar 7, 2024	Anomaly DetectionIntrusion Detection	—Unverified
MLAD: A Unified Model for Multi-system Log Anomaly Detection	Jan 15, 2024	Anomaly DetectionRelational Reasoning	—Unverified
ML-based Anomaly Detection in Optical Fiber Monitoring	Feb 23, 2022	Anomaly Detection	—Unverified
MLOps with Microservices: A Case Study on the Maritime Domain	Jun 6, 2025	Anomaly Detection	—Unverified
Model-Based Monitoring and State Estimation for Digital Twins: The Kalman Filter	Apr 29, 2023	Anomaly DetectionFault Detection	—Unverified
Model-driven Engineering for Machine Learning Components: A Systematic Literature Review	Nov 1, 2023	Anomaly DetectionDecision Making	—Unverified
Model Evaluation and Anomaly Detection in Temporal Complex Networks using Deep Learning Methods	Jun 15, 2024	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	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