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 11–20 of 4856 papers

Title	Date	Tasks	Status
Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection	Jul 5, 2025	3D Anomaly DetectionAnomaly Detection	—Unverified
Evaluating Language Models For Threat Detection in IoT Security Logs	Jul 3, 2025	Anomaly Detection	CodeCode Available
Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks	Jun 26, 2025	Anomaly DetectionGenerative Adversarial Network	—Unverified
Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems	Jun 26, 2025	Anomaly DetectionFault Diagnosis	—Unverified
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale	Jun 26, 2025	Anomaly DetectionBenchmarking	CodeCode Available
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection	Jun 26, 2025	Anomaly DetectionFeature Engineering	—Unverified
FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection	Jun 26, 2025	Anomaly DetectionComputational Efficiency	—Unverified
SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark	Jun 26, 2025	3D Anomaly Detection3D Anomaly Detection and Segmentation	—Unverified
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models	Jun 26, 2025	Anomaly DetectionBrain Tumor Segmentation	—Unverified
E-ABIN: an Explainable module for Anomaly detection in BIological Networks	Jun 25, 2025	Anomaly Detection	CodeCode Available

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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